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| canonical correlation analysis python: Python for Accounting and Finance Sunil Kumar, |
| canonical correlation analysis python: Applied Univariate, Bivariate, and Multivariate Statistics Using Python Daniel J. Denis, 2021-07-14 Applied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied. Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python. Readers will also benefit from the inclusion of: A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors An introduction to Python, exploring how to communicate with Python A treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management An introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological level Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python. |
| canonical correlation analysis python: Practical Machine Learning for Data Analysis Using Python Abdulhamit Subasi, 2020-06-05 Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features |
| canonical correlation analysis python: Python 3 for Machine Learning Oswald Campesato, 2020-02-07 This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is devoted to machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on NLP and RL. Keras-based code samples are included to supplement the theoretical discussion. The book also contains separate appendices for regular expressions, Keras, and TensorFlow 2. Features: Provides the reader with basic Python 3 programming concepts related to machine learning Includes separate appendices for regular expressions, Keras, and TensorFlow 2 |
| canonical correlation analysis python: Python 3 and Feature Engineering Oswald Campesato, 2023-12-12 This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you’ll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you’ll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework. FEATURES Includes numerous practical examples and partial code blocks that illuminate the path from theory to application Explores everything from data cleaning to the subtleties of feature selection and extraction, covering a wide spectrum of feature engineering topics Offers an appendix on working with the “awk” command-line utility Features companion files available for downloading with source code, datasets, and figures |
| canonical correlation analysis python: Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance Ankur Kumar, Jesus Flores-Cerrillo, 2024-01-12 This book is designed to help readers quickly gain a working knowledge of machine learning-based techniques that are widely employed for building equipment condition monitoring, plantwide monitoring , and predictive maintenance solutions in process industry . The book covers a broad spectrum of techniques ranging from univariate control charts to deep learning-based prediction of remaining useful life. Consequently, the readers can leverage the concepts learned to build advanced solutions for fault detection, fault diagnosis, and fault prognosis. The application focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers and data scientists. Upon completion, readers will be able to confidently navigate the Prognostics and Health Management literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into seven parts. Part 1 lays down the basic foundations of ML-assisted process and equipment condition monitoring, and predictive maintenance. Part 2 provides in-detail presentation of classical ML techniques for univariate signal monitoring. Different types of control charts and time-series pattern matching methodologies are discussed. Part 3 is focused on the widely popular multivariate statistical process monitoring (MSPM) techniques. Emphasis is paid to both the fault detection and fault isolation/diagnosis aspects. Part 4 covers the process monitoring applications of classical machine learning techniques such as k-NN, isolation forests, support vector machines, etc. These techniques come in handy for processes that cannot be satisfactorily handled via MSPM techniques. Part 5 navigates the world of artificial neural networks (ANN) and studies the different ANN structures that are commonly employed for fault detection and diagnosis in process industry. Part 6 focusses on vibration-based monitoring of rotating machinery and Part 7 deals with prognostic techniques for predictive maintenance applications. Broadly, the book covers the following: Exploratory analysis of process data Best practices for process monitoring and predictive maintenance solutions Univariate monitoring via control charts and time series data mining Multivariate statistical process monitoring techniques (PCA, PLS, FDA, etc.) Machine learning and deep learning techniques to handle dynamic, nonlinear, and multimodal processes Fault detection and diagnosis of rotating machinery using vibration data Remaining useful life predictions for predictive maintenance |
| canonical correlation analysis python: Data Augmentation with Python Duc Haba, 2023-04-28 Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source libraries Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore beautiful, customized charts and infographics in full color Work with fully functional OO code using open source libraries in the Python Notebook for each chapter Unleash the potential of real-world datasets with practical data augmentation techniques Book Description Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset. The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You'll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you'll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges. By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques. What you will learn Write OOP Python code for image, text, audio, and tabular data Access over 150,000 real-world datasets from the Kaggle website Analyze biases and safe parameters for each augmentation method Visualize data using standard and exotic plots in color Discover 32 advanced open source augmentation libraries Explore machine learning models, such as BERT and Transformer Meet Pluto, an imaginary digital coding companion Extend your learning with fun facts and fun challenges Who this book is for This book is for data scientists and students interested in the AI discipline. Advanced AI or deep learning skills are not required; however, knowledge of Python programming and familiarity with Jupyter Notebooks are essential to understanding the topics covered in this book. |
| canonical correlation analysis python: Python 3 and Machine Learning Using ChatGPT / GPT-4 Oswald Campesato, 2024-05-22 This book is designed to bridge the gap between theoretical knowledge and practical application in the fields of Python programming, machine learning, and the innovative use of ChatGPT-4 in data science. The book is structured to facilitate a deep understanding of several core topics. It begins with a detailed introduction to Pandas, a cornerstone Python library for data manipulation and analysis. Next, it explores a variety of machine learning classifiers from kNN to SVMs. In later chapters, it discusses the capabilities of GPT-4, and how its application enhances traditional linear regression analysis. Finally, the book covers the innovative use of ChatGPT in data visualization. This segment focuses on how AI can transform data into compelling visual stories, making complex results accessible and understandable. It includes material on AI apps, GANs, and DALL-E. Companion files are available for downloading with code and figures from the text. FEATURES: Includes practical tutorials designed to provide hands-on experience, reinforcing learning through practice Provides coverage of the latest Python tools using state-of-the-art libraries essential for modern data scientists Companion files with source code, datasets, and figures are available for downloading |
| canonical correlation analysis python: C, C++, Java, Python, PHP, JavaScript and Linux For Beginners Manjunath.R, 2020-04-13 An Introduction to Programming Languages and Operating Systems for Novice Coders An ideal addition to your personal elibrary. With the aid of this indispensable reference book, you may quickly gain a grasp of Python, Java, JavaScript, C, C++, CSS, Data Science, HTML, LINUX and PHP. It can be challenging to understand the programming language's distinctive advantages and charms. Many programmers who are familiar with a variety of languages frequently approach them from a constrained perspective rather than enjoying their full expressivity. Some programmers incorrectly use Programmatic features, which can later result in serious issues. The programmatic method of writing programs—the ideal approach to use programming languages—is explained in this book. This book is for all programmers, whether you are a novice or an experienced pro. Its numerous examples and well paced discussions will be especially beneficial for beginners. Those who are already familiar with programming will probably gain more from this book, of course. I want you to be prepared to use programming to make a big difference. C, C++, Java, Python, PHP, JavaScript and Linux For Beginners is a comprehensive guide to programming languages and operating systems for those who are new to the world of coding. This easy-to-follow book is designed to help readers learn the basics of programming and Linux operating system, and to gain confidence in their coding abilities. With clear and concise explanations, readers will be introduced to the fundamental concepts of programming languages such as C, C++, Java, Python, PHP, and JavaScript, as well as the basics of the Linux operating system. The book offers step-by-step guidance on how to write and execute code, along with practical exercises that help reinforce learning. Whether you are a student or a professional, C, C++, Java, Python, PHP, JavaScript and Linux For Beginners provides a solid foundation in programming and operating systems. By the end of this book, readers will have a solid understanding of the core concepts of programming and Linux, and will be equipped with the knowledge and skills to continue learning and exploring the exciting world of coding. |
| canonical correlation analysis python: An Introduction to Model-Based Cognitive Neuroscience Birte U. Forstmann, |
| canonical correlation analysis python: Statistical Methods for Climate Scientists Timothy DelSole, Michael Tippett, 2022-02-24 An accessible introduction to statistical methods for students in the climate sciences. |
| canonical correlation analysis python: Image Analysis, Classification and Change Detection in Remote Sensing Morton J. Canty, 2014-06-06 Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition introduces techniques used in the processing of remote sensing digital imagery. It emphasizes the development and implementation of statistically motivated, data-driven techniques. The author achieves this by tightly interweaving theory, algorithms, and computer codes. See What’s New in the Third Edition: Inclusion of extensive code in Python, with a cloud computing example New material on synthetic aperture radar (SAR) data analysis New illustrations in all chapters Extended theoretical development The material is self-contained and illustrated with many programming examples in IDL. The illustrations and applications in the text can be plugged in to the ENVI system in a completely transparent fashion and used immediately both for study and for processing of real imagery. The inclusion of Python-coded versions of the main image analysis algorithms discussed make it accessible to students and teachers without expensive ENVI/IDL licenses. Furthermore, Python platforms can take advantage of new cloud services that essentially provide unlimited computational power. The book covers both multispectral and polarimetric radar image analysis techniques in a way that makes both the differences and parallels clear and emphasizes the importance of choosing appropriate statistical methods. Each chapter concludes with exercises, some of which are small programming projects, intended to illustrate or justify the foregoing development, making this self-contained text ideal for self-study or classroom use. |
| canonical correlation analysis python: An Introduction to Multivariate Statistical Analysis T. W. Anderson, 2003-07-25 Perfected over three editions and more than forty years, this field- and classroom-tested reference: * Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. * Treats all the basic and important topics in multivariate statistics. * Adds two new chapters, along with a number of new sections. * Provides the most methodical, up-to-date information on MV statistics available. |
| canonical correlation analysis python: High Performance Computing for Computational Science - VECPAR 2006 Michel Daydé, 2007-04-02 This book constitutes the thoroughly refereed post-proceedings of the 7th International Conference on High Performance Computing for Computational Science, VECPAR 2006, held in Rio de Janeiro, Brazil, in June 2006. The 44 revised full papers presented together with one invited paper and 12 revised workshop papers cover Grid computing, cluster computing, numerical methods, large-scale simulations in Physics, and computing in Biosciences. |
| canonical correlation analysis python: Statistical Learning with Sparsity Trevor Hastie, Robert Tibshirani, Martin Wainwright, 2015-05-07 Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underl |
| canonical correlation analysis python: Computational Systems Biology in Medicine and Biotechnology Sonia Cortassa, Miguel A. Aon, 2022-05-23 This volume addresses the latest state-of-the-art systems biology-oriented approaches that--driven by big data and bioinformatics--are utilized by Computational Systems Biology, an interdisciplinary field that bridges experimental tools with computational tools to tackle complex questions at the frontiers of knowledge in medicine and biotechnology. The chapters in this book are organized into six parts: systems biology of the genome, epigenome, and redox proteome; metabolic networks; aging and longevity; systems biology of diseases; spatiotemporal patterns of rhythms, morphogenesis, and complex dynamics; and genome scale metabolic modeling in biotechnology. In every chapter, readers will find varied methodological approaches applied at different levels, from molecular, cellular, organ to organisms, genome to phenome, and health and disease. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics; criteria utilized for applying specific methodologies; lists of the necessary materials, reagents, software, databases, algorithms, mathematical models, and dedicated analytical procedures; step-by-step, readily reproducible laboratory, bioinformatics, and computational protocols all delivered in didactic and clear style and abundantly illustrated with express case studies and tutorials; and tips on troubleshooting and advice for achieving reproducibility while avoiding mistakes and misinterpretations. The overarching goal driving this volume is to excite the expert and stimulate the newcomer to the field of Computational Systems Biology. Cutting-edge and authoritative, Computational Systems Biology in Medicine and Biotechnology: Methods and Protocols is a valuable resource for pre- and post-graduate students in medicine and biotechnology, and in diverse areas ranging from microbiology to cellular and organismal biology, as well as computational and experimental biologists, and researchers interested in utilizing comprehensive systems biology oriented methods. |
| canonical correlation analysis python: Advances in Computational Intelligence Ignacio Rojas, Gonzalo Joya, Andreu Catala, 2023-11-01 This two-volume set LNCS 14134 and LNCS 14135 constitutes the refereed proceedings of the 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, held in Ponta Delgada, Portugal, during June 19–21, 2023. The 108 full papers presented in this two-volume set were carefully reviewed and selected from 149 submissions. The papers in Part I are organized in topical sections on advanced topics in computational intelligence; advances in artificial neural networks; ANN HW-accelerators; applications of machine learning in biomedicine and healthcare; and applications of machine learning in time series analysis. The papers in Part II are organized in topical sections on deep learning and applications; deep learning applied to computer vision and robotics; general applications of artificial intelligence; interaction with neural systems in both health and disease; machine learning for 4.0 industry solutions; neural networks in chemistry and material characterization; ordinal classification; real world applications of BCI systems; and spiking neural networks: applications and algorithms. |
| canonical correlation analysis python: Immunometabolic Mechanisms Underlying the Severity of COVID-19 Galileo Escobedo, Vishwanath Venketaraman, Julia Kzhyshkowska, 2022-08-18 |
| canonical correlation analysis python: Text Analytics with Python Dipanjan Sarkar, 2016-11-30 Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data |
| canonical correlation analysis python: Handbook of Research Methods and Applications in Experimental Economics Arthur Schram, Aljaž Ule, 2019 This volume offers a comprehensive review of experimental methods in economics. Its 21 chapters cover theoretical and practical issues such as incentives, theory and policy development, data analysis, recruitment, software and laboratory organization. The Handbook includes separate parts on procedures, field experiments and neuroeconomics, and provides the first methodological overview of replication studies and a novel set-valued equilibrium concept. As a whole, the combination of basic methods and current developments will aid both beginners and advanced experimental economists. |
| canonical correlation analysis python: Cognitive NeuroIntelligence Jia Liu, Si Wu, Ke Zhou, Yiying Song, 2021-09-23 |
| canonical correlation analysis python: Computational Medicine in Data Mining and Modeling Goran Rakocevic, Tijana Djukic, Nenad Filipovic, Veljko Milutinović, 2013-10-17 This book presents an overview of a variety of contemporary statistical, mathematical and computer science techniques which are used to further the knowledge in the medical domain. The authors focus on applying data mining to the medical domain, including mining the sets of clinical data typically found in patient’s medical records, image mining, medical mining, data mining and machine learning applied to generic genomic data and more. This work also introduces modeling behavior of cancer cells, multi-scale computational models and simulations of blood flow through vessels by using patient-specific models. The authors cover different imaging techniques used to generate patient-specific models. This is used in computational fluid dynamics software to analyze fluid flow. Case studies are provided at the end of each chapter. Professionals and researchers with quantitative backgrounds will find Computational Medicine in Data Mining and Modeling useful as a reference. Advanced-level students studying computer science, mathematics, statistics and biomedicine will also find this book valuable as a reference or secondary text book. |
| canonical correlation analysis python: Digital Transformation of Collaboration Aleksandra Przegalinska, Francesca Grippa, Peter A. Gloor, 2020-07-28 This proceedings is focused on the emerging concept of Collaborative Innovation Networks (COINs). COINs are at the core of collaborative knowledge networks, distributed communities taking advantage of the wide connectivity and the support of communication technologies, spanning beyond the organizational perimeter of companies on a global scale. The book presents the refereed conference papers from the 7th International Conference on COINs, October 8-9, 2019, in Warsaw, Poland. It includes papers for both application areas of COINs, (1) optimizing organizational creativity and performance, and (2) discovering and predicting new trends by identifying COINs on the Web through online social media analysis. Papers at COINs19 combine a wide range of interdisciplinary fields such as social network analysis, group dynamics, design and visualization, information systems and the psychology and sociality of collaboration, and intercultural analysis through the lens of online social media. They will cover most recent advances in areas from leadership and collaboration, trend prediction and data mining, to social competence and Internet communication. |
| canonical correlation analysis python: Image Analysis, Classification and Change Detection in Remote Sensing Morton John Canty, 2019-03-11 Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for Python, Fourth Edition, is focused on the development and implementation of statistically motivated, data-driven techniques for digital image analysis of remotely sensed imagery and it features a tight interweaving of statistical and machine learning theory of algorithms with computer codes. It develops statistical methods for the analysis of optical/infrared and synthetic aperture radar (SAR) imagery, including wavelet transformations, kernel methods for nonlinear classification, as well as an introduction to deep learning in the context of feed forward neural networks. New in the Fourth Edition: An in-depth treatment of a recent sequential change detection algorithm for polarimetric SAR image time series. The accompanying software consists of Python (open source) versions of all of the main image analysis algorithms. Presents easy, platform-independent software installation methods (Docker containerization). Utilizes freely accessible imagery via the Google Earth Engine and provides many examples of cloud programming (Google Earth Engine API). Examines deep learning examples including TensorFlow and a sound introduction to neural networks, Based on the success and the reputation of the previous editions and compared to other textbooks in the market, Professor Canty’s fourth edition differs in the depth and sophistication of the material treated as well as in its consistent use of computer codes to illustrate the methods and algorithms discussed. It is self-contained and illustrated with many programming examples, all of which can be conveniently run in a web browser. Each chapter concludes with exercises complementing or extending the material in the text. |
| canonical correlation analysis python: Artificial Intelligence Sandeep Reddy, 2020-12-02 The rediscovery of the potential of artificial intelligence (AI) to improve healthcare delivery and patient outcomes has led to an increasing application of AI techniques such as deep learning, computer vision, natural language processing, and robotics in the healthcare domain. Many governments and health authorities have prioritized the application of AI in the delivery of healthcare. Also, technological giants and leading universities have established teams dedicated to the application of AI in medicine. These trends will mean an expanded role for AI in the provision of healthcare. Yet, there is an incomplete understanding of what AI is and its potential for use in healthcare. This book discusses the different types of AI applicable to healthcare and their application in medicine, population health, genomics, healthcare administration, and delivery. Readers, especially healthcare professionals and managers, will find the book useful to understand the different types of AI and how they are relevant to healthcare delivery. The book provides examples of AI being applied in medicine, population health, genomics, healthcare administration, and delivery and how they can commence applying AI in their health services. Researchers and technology professionals will also find the book useful to note current trends in the application of AI in healthcare and initiate their own projects to enable the application of AI in healthcare/medical domains. |
| canonical correlation analysis python: Artificial Neural Networks and Machine Learning – ICANN 2020 Igor Farkaš, Paolo Masulli, Stefan Wermter, 2020-10-19 The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.* The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic. |
| canonical correlation analysis python: Introduction to High-Dimensional Statistics Christophe Giraud, 2021-08-25 Praise for the first edition: [This book] succeeds singularly at providing a structured introduction to this active field of research. ... it is arguably the most accessible overview yet published of the mathematical ideas and principles that one needs to master to enter the field of high-dimensional statistics. ... recommended to anyone interested in the main results of current research in high-dimensional statistics as well as anyone interested in acquiring the core mathematical skills to enter this area of research. —Journal of the American Statistical Association Introduction to High-Dimensional Statistics, Second Edition preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities. High-dimensional statistics is a fast-evolving field, and much progress has been made on a large variety of topics, providing new insights and methods. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new edition: Offers revised chapters from the previous edition, with the inclusion of many additional materials on some important topics, including compress sensing, estimation with convex constraints, the slope estimator, simultaneously low-rank and row-sparse linear regression, or aggregation of a continuous set of estimators. Introduces three new chapters on iterative algorithms, clustering, and minimax lower bounds. Provides enhanced appendices, minimax lower-bounds mainly with the addition of the Davis-Kahan perturbation bound and of two simple versions of the Hanson-Wright concentration inequality. Covers cutting-edge statistical methods including model selection, sparsity and the Lasso, iterative hard thresholding, aggregation, support vector machines, and learning theory. Provides detailed exercises at the end of every chapter with collaborative solutions on a wiki site. Illustrates concepts with simple but clear practical examples. |
| canonical correlation analysis python: The Open Handbook of Linguistic Data Management Andrea L. Berez-Kroeker, Bradley McDonnell, Eve Koller, Lauren B. Collister, 2022-01-18 A guide to principles and methods for the management, archiving, sharing, and citing of linguistic research data, especially digital data. Doing language science depends on collecting, transcribing, annotating, analyzing, storing, and sharing linguistic research data. This volume offers a guide to linguistic data management, engaging with current trends toward the transformation of linguistics into a more data-driven and reproducible scientific endeavor. It offers both principles and methods, presenting the conceptual foundations of linguistic data management and a series of case studies, each of which demonstrates a concrete application of abstract principles in a current practice. In part 1, contributors bring together knowledge from information science, archiving, and data stewardship relevant to linguistic data management. Topics covered include implementation principles, archiving data, finding and using datasets, and the valuation of time and effort involved in data management. Part 2 presents snapshots of practices across various subfields, with each chapter presenting a unique data management project with generalizable guidance for researchers. The Open Handbook of Linguistic Data Management is an essential addition to the toolkit of every linguist, guiding researchers toward making their data FAIR: Findable, Accessible, Interoperable, and Reusable. |
| canonical correlation analysis python: Applied Parallel Computing Bo Kagström, Erik Elmroth, Jack Dongarra, Jerzy Wasniewski, 2007-09-22 This book constitutes the thoroughly refereed post-proceedings of the 8th International Workshop on Applied Parallel Computing, PARA 2006. It covers partial differential equations, parallel scientific computing algorithms, linear algebra, simulation environments, algorithms and applications for blue gene/L, scientific computing tools and applications, parallel search algorithms, peer-to-peer computing, mobility and security, algorithms for single-chip multiprocessors. |
| canonical correlation analysis python: Cognitive Science, Computational Intelligence, and Data Analytics Vikas Khare, Sanjeet Kumar Dwivedi, Monica Bhatia, 2024-06-06 Cognitive Science, Computational Intelligence, and Data Analytics: Methods and Applications with Python introduces readers to the foundational concepts of data analysis, cognitive science, and computational intelligence, including AI and Machine Learning. The book's focus is on fundamental ideas, procedures, and computational intelligence tools that can be applied to a wide range of data analysis approaches, with applications that include mathematical programming, evolutionary simulation, machine learning, and logic-based models. It offers readers the fundamental and practical aspects of cognitive science and data analysis, exploring data analytics in terms of description, evolution, and applicability in real-life problems.The authors cover the history and evolution of cognitive analytics, methodological concerns in philosophy, syntax and semantics, understanding of generative linguistics, theory of memory and processing theory, structured and unstructured data, qualitative and quantitative data, measurement of variables, nominal, ordinals, intervals, and ratio scale data. The content in this book is tailored to the reader's needs in terms of both type and fundamentals, including coverage of multivariate analysis, CRISP methodology and SEMMA methodology. Each chapter provides practical, hands-on learning with real-world applications, including case studies and Python programs related to the key concepts being presented. - Demystifies the theory of data analytics using a step-by-step approach - Covers the intersection of cognitive science, computational intelligence, and data analytics by providing examples and case studies with applied algorithms, mathematics, and Python programming code - Introduces foundational data analytics techniques such as CRISP-DM, SEMMA, and Object Detection Models in the context of computational intelligence methods and tools - Covers key concepts of multivariate and cognitive data analytics such as factor analytics, principal component analytics, linear regression analysis, logistic regression analysis, and value chain applications |
| canonical correlation analysis python: Machine Learning Theory and Applications Xavier Vasques, 2024-01-11 Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra. |
| canonical correlation analysis python: ICDSMLA 2020 Amit Kumar, Sabrina Senatore, Vinit Kumar Gunjan, 2021-11-08 This book gathers selected high-impact articles from the 2nd International Conference on Data Science, Machine Learning & Applications 2020. It highlights the latest developments in the areas of artificial intelligence, machine learning, soft computing, human–computer interaction and various data science and machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise. |
| canonical correlation analysis python: Handbook of Biomarkers and Precision Medicine Claudio Carini, Mark Fidock, Alain van Gool, 2019-04-16 The field of Biomarkers and Precision Medicine in drug development is rapidly evolving and this book presents a snapshot of exciting new approaches. By presenting a wide range of biomarker applications, discussed by knowledgeable and experienced scientists, readers will develop an appreciation of the scope and breadth of biomarker knowledge and find examples that will help them in their own work. -Maria Freire, Foundation for the National Institutes of Health Handbook of Biomarkers and Precision Medicine provides comprehensive insights into biomarker discovery and development which has driven the new era of Precision Medicine. A wide variety of renowned experts from government, academia, teaching hospitals, biotechnology and pharmaceutical companies share best practices, examples and exciting new developments. The handbook aims to provide in-depth knowledge to research scientists, students and decision makers engaged in Biomarker and Precision Medicine-centric drug development. Features: Detailed insights into biomarker discovery, validation and diagnostic development with implementation strategies Lessons-learned from successful Precision Medicine case studies A variety of exciting and emerging biomarker technologies The next frontiers and future challenges of biomarkers in Precision Medicine Claudio Carini, Mark Fidock and Alain van Gool are internationally recognized as scientific leaders in Biomarkers and Precision Medicine. They have worked for decades in academia and pharmaceutical industry in EU, USA and Asia. Currently, Dr. Carini is Honorary Faculty at Kings’s College School of Medicine, London, UK. Dr. Fidock is Vice President of Precision Medicine Laboratories at AstraZeneca, Cambridge, UK. Prof.dr. van Gool is Head Translational Metabolic Laboratory at Radboud university medical school, Nijmegen, NL. |
| canonical correlation analysis python: Food Insecurity & Hydroclimate in Greater Horn of Africa Joseph Awange, 2022-01-25 This book will benefit users in food security, agriculture, water management, and environmental sectors. It provides the first comprehensive analysis of Greater Horn of Africa (GHA)’s food insecurity and hydroclimate using the state-of-the-art Gravity Recovery and Climate Experiment (GRACE) and its Follow-on (GRACE-FO)’s, centennial precipitation, hydrological models’ and reanalysis’ products. It is here opined that GHA is endowed with freshwater (surface and groundwater) being home to the world's second largest freshwater body (Lake Victoria) and the greatest continental water towers (Ethiopian Highlands) that if properly tapped in a sustainable way, will support its irrigated agriculture as well as pastoralism. First, however, the obsolete Nile treaties that hamper the use of Lake Victoria (White Nile) and Ethiopian Highland (Blue Nile) have to be unlocked. Moreover, GHA is bedevilled by poor governance and the ``donor-assistance” syndrome; and in 2020-2021 faced the so-called ``triple threats’’ of desert locust infestation, climate variability/change impacts and COVID-19 pandemic. Besides, climate extremes influence its meagre waters leading to perennial food insecurity. Coupled with frequent regional and local conflicts, high population growth rate, low crop yield, invasion of migratory pests, contagious human and livestock diseases (such as HIV/AIDs, COVID-19 & Rift Valley fever) and poverty, life for more than 310 million of its inhabitants simply becomes unbearable. Alarming also is the fact that drought-like humanitarian crises are increasing in GHA despite recent progress in its monitoring and prediction efforts. Notwithstanding these efforts, there remain challenges stemming from uncertainty in its prediction, and the inflexibility and limited buffering capacity of the recurrent impacted systems. To achieve greater food security, therefore, in addition to boosting GHA's agricultural output, UN Office for the Coordination of Humanitarian Affairs suggest that its “inhabitants must create more diverse and stable means of livelihood to insulate themselves and their households from external shocks”. This is a task that they acknowledge will not be easy as the path ahead is “strewn with obstacles namely; natural hazards and armed conflicts”. Understanding GHA’s food insecurity and its hydroclimate as presented in this book is a good starting point towards managing the impacts of the natural hazards on the one hand while understanding the impacts associated with extreme climate on GHA's available water and assessing the potential of its surface and groundwater to support its irrigated agriculture and pastoralism would be the first step towards “coping with drought” on the other hand. The book represents a significant effort by Prof Awange in trying to offer a comprehensive overview of the hydroclimate in the Greater Horn of Africa (GHA). Prof Eric F. Wood, NAE (USA); FRSC (Canada); Foreign member, ATSE (Australia). |
| canonical correlation analysis python: Advances in Intelligent Data Analysis XXI Bruno Crémilleux, Sibylle Hess, Siegfried Nijssen, 2023-03-31 This book constitutes the proceedings of the 21st International Symposium on Intelligent Data Analysis, IDA 2022, which was held in Louvain-la-Neuve, Belgium, during April 12-14, 2023. The 38 papers included in this book were carefully reviewed and selected from 91 submissions. IDA is an international symposium presenting advances in the intelligent analysis of data. Distinguishing characteristics of IDA are its focus on novel, inspiring ideas, its focus on research, and its relatively small scale. |
| canonical correlation analysis python: Neural Information Processing Derong Liu, Shengli Xie, Yuanqing Li, Dongbin Zhao, El-Sayed M. El-Alfy, 2017-11-07 The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017. The 563 full papers presented were carefully reviewed and selected from 856 submissions. The 6 volumes are organized in topical sections on Machine Learning, Reinforcement Learning, Big Data Analysis, Deep Learning, Brain-Computer Interface, Computational Finance, Computer Vision, Neurodynamics, Sensory Perception and Decision Making, Computational Intelligence, Neural Data Analysis, Biomedical Engineering, Emotion and Bayesian Networks, Data Mining, Time-Series Analysis, Social Networks, Bioinformatics, Information Security and Social Cognition, Robotics and Control, Pattern Recognition, Neuromorphic Hardware and Speech Processing. |
| canonical correlation analysis python: Microbes from Marine Distinctive Environments Shan He, Ming Ma, Slava Epstein, 2023-12-18 |
| canonical correlation analysis python: Plant Disease Management in the Post-Genomic Era: From Functional Genomics to Genome Editing Sabrina Sarrocco, Alfredo Herrera-Estrella, David B. Collinge, 2020-03-16 |
| canonical correlation analysis python: Quantifying Uncertainty in Subsurface Systems Céline Scheidt, Lewis Li, Jef Caers, 2018-04-27 Under the Earth’s surface is a rich array of geological resources, many with potential use to humankind. However, extracting and harnessing them comes with enormous uncertainties, high costs, and considerable risks. The valuation of subsurface resources involves assessing discordant factors to produce a decision model that is functional and sustainable. This volume provides real-world examples relating to oilfields, geothermal systems, contaminated sites, and aquifer recharge. Volume highlights include: • A multi-disciplinary treatment of uncertainty quantification • Case studies with actual data that will appeal to methodology developers • A Bayesian evidential learning framework that reduces computation and modeling time Quantifying Uncertainty in Subsurface Systems is a multidisciplinary volume that brings together five major fields: information science, decision science, geosciences, data science and computer science. It will appeal to both students and practitioners, and be a valuable resource for geoscientists, engineers and applied mathematicians. Read the Editors’ Vox: https://eos.org/editors-vox/quantifying-uncertainty-about-earths-resources |
| canonical correlation analysis python: Research Techniques in Psychology PressGrup Academician Team, ANOVA is an essential statistical technique in psychological research, enabling psychologists to analyze differences across multiple groups while controlling for Type I error. Mastery of ANOVA, including its various types, assumptions, and reporting standards, is vital for quantitative researchers in psychology. By employing robust research designs and adhering to the assumptions underlying ANOVA, researchers can derive meaningful insights into complex psychological phenomena, ultimately contributing to the advancement of psychological science. In summary, proficient use of ANOVA techniques encompassed within a thorough understanding of their methodology will empower researchers to make informed decisions, accurate interpretations, and substantial contributions to the field of psychology. |
数学上通常是什么原因叫一件东西做「canonical」？ - 知乎
Jan 31, 2018 · canon本身也是拉丁语单词，意思是律法，带有一定的宗教意味。所以canonical，形象地来说可以解释为是 天选的。 如果一个对象，它出现在这里或者选择这个特 …
Canonical - Search Console Help
A canonical URL is the URL of the best representative page from a group of duplicate pages, according to ...
Google Search index link [canonical_link]
If you do not indicate a canonical link, Google will crawl your site and set whichever URL it considers to be the most representative as the canonical link. If you already provide a Google …
About CNAME records - Google Workspace Admin Help
A Canonical Name or CNAME record is a type of DNS record that maps an alias name to a true or canonical domain name. CNAME records are typically used to map a subdomain such as www …
Ubuntu 的发行商 Canonical 公司是靠什么赚钱的？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
How can I remove a Google chosen canonical for a url that no …
This help content & information General Help Center experience. Search. Clear search
"Duplicate without user-selected canonical" error and wrong …
Jul 10, 2023 · This help content & information General Help Center experience. Search. Clear search
How to change User-declared canonical? - Google Help
This help content & information General Help Center experience. Search. Clear search
how to fix Alternate page with proper canonical tag
Jul 28, 2021 · This help content & information General Help Center experience. Search. Clear search
Canonical和Ubuntu有什么关系？ - 知乎
May 10, 2015 · Canonical公司是由马克创建的，主要为了促进开源软件项目，Ubuntu作为公司最重要的项目，这并不冲突。 Ubuntu的确是开源的软件，但是在Canonical公司的大力开发和推 …
数学上通常是什么原因叫一件东西做「canonical」？ - 知乎
Jan 31, 2018 · canon本身也是拉丁语单词，意思是律法，带有一定的宗教意味。所以canonical，形象地来说可以解释为是 天选的。 如果一个对象，它出现在这里或者选择这个特 …
Canonical - Search Console Help
A canonical URL is the URL of the best representative page from a group of duplicate pages, according to ...
Google Search index link [canonical_link]
If you do not indicate a canonical link, Google will crawl your site and set whichever URL it considers to be the most representative as the canonical link. If you already provide a Google …
About CNAME records - Google Workspace Admin Help
A Canonical Name or CNAME record is a type of DNS record that maps an alias name to a true or canonical domain name. CNAME records are typically used to map a subdomain such as www …
Ubuntu 的发行商 Canonical 公司是靠什么赚钱的？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
How can I remove a Google chosen canonical for a url that no …
This help content & information General Help Center experience. Search. Clear search
"Duplicate without user-selected canonical" error and wrong …
Jul 10, 2023 · This help content & information General Help Center experience. Search. Clear search
How to change User-declared canonical? - Google Help
This help content & information General Help Center experience. Search. Clear search
how to fix Alternate page with proper canonical tag
Jul 28, 2021 · This help content & information General Help Center experience. Search. Clear search
Canonical和Ubuntu有什么关系？ - 知乎
May 10, 2015 · Canonical公司是由马克创建的，主要为了促进开源软件项目，Ubuntu作为公司最重要的项目，这并不冲突。 Ubuntu的确是开源的软件，但是在Canonical公司的大力开发和推 …
