Behavior analysis with machine learning using r, Ceja, Enrique Garcia
Автор: Woolf, Nicholas H. Silver, Christina (university O Название: Qualitative analysis using nvivo ISBN: 1138743674 ISBN-13(EAN): 9781138743670 Издательство: Taylor&Francis Рейтинг: Цена: 5358.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The method of Five-Level QDA (R) helps researchers to analyse what makes for an expert analytic performance when using qualitative data analysis software. Written by experienced trainers, this practical guide to NVivo will allow qualitative researchers to increase the effectiveness of their data analysis.
Автор: Bailey Jon S Название: Research Methods in Applied Behavior Analysis ISBN: 1138685267 ISBN-13(EAN): 9781138685260 Издательство: Taylor&Francis Рейтинг: Цена: 7195.00 р. Наличие на складе: Поставка под заказ.
Описание: Research Methods in Applied Behavior Analysis, 2nd Edition, is a practical text that provides the beginning researcher with a clear description of how behavior analysts conduct applied research and submit it for publication.
Автор: Pages Название: Multiple Factor Analysis by Example Using R ISBN: 1482205475 ISBN-13(EAN): 9781482205473 Издательство: Taylor&Francis Рейтинг: Цена: 13014.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR). The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The next chapter discusses factor analysis for mixed data (FAMD), a little-known method for simultaneously analyzing quantitative and qualitative variables without group distinction. Focusing on MFA, subsequent chapters examine the key points of MFA in the context of quantitative variables as well as qualitative and mixed data. The author also compares MFA and Procrustes analysis and presents a natural extension of MFA: hierarchical MFA (HMFA). The final chapter explores several elements of matrix calculation and metric spaces used in the book.
Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.
New to the Second Edition
The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows
New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics
New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping
New chapter on simulation that includes examples of data generated from complex models and distributions
A detailed discussion of the philosophy and use of the knitr and markdown packages for R
New packages that extend the functionality of R and facilitate sophisticated analyses
Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots
Easily Find Your Desired Task
Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.
Автор: Simone Marinai; Hiromichi Fujisawa Название: Machine Learning in Document Analysis and Recognition ISBN: 3642095119 ISBN-13(EAN): 9783642095115 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and state-of-the-art reviews by leading researchers all over the world.
Автор: Subasi, Abdulhamit Название: Practical Machine Learning For Data Analysis Using Python ISBN: 0128213795 ISBN-13(EAN): 9780128213797 Издательство: Elsevier Science Рейтинг: Цена: 16505.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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.
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more.
This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis.
Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction
Explains how to apply machine learning techniques to EEG, ECG and EMG signals
Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series
Описание: This book provides a detailed description of the entire study process concerning gathering and analysing big data and making observations to develop a crime-prediction model that utilises its findings.
1. Time and Self-similar Structure in Behavior and Interactions: From Sequences to Symmetry and Fractals. Magnusson M.S.
2. Interactive Deception In Group Decision-Making: New Insights From Communication Pattern Analysis. Burgoon J., et al.
3. Imposing Cognitive Load To Detect Prepared Lies: A T-Pattern Approach. Zurloni V., et al.
4. Paraverbal Communicative Teaching T-patterns using SOCIN and SOPROX observational systems. Castaсer M., et al.
5. The Self-Organization of Self-Injurious Behavior as Revealed Through Temporal Pattern Analyses. Kemp A., et al.
6. Detecting and Characterizing Patterns of Behavioral Symptoms of Dementia. Woods D.L., et al.
7. Typical errors and behavioral sequences in judo techniques: knowledge of performance and the analysis of T-patterns in relation to teaching and learning the Ouchi-gari throw. Prieto I., et al.
8. Qualitative differences in men's and women's facial movements in an experimental situation. Racca A., et al.
9. Understanding Film Art: Moments Of Impact And Patterns Of Reactions. Suckfьll M. and Unz D
10. Immersive Dynamics: Presence Experiences And Patterns Of Attention. M. Brill et al.
11. Accessing Individual Style Th
rough Proposed Use Of Theme Associates. Quaeghebeur L. and McNeill D. Part 2 - Animal and Neuronal Behavior (Non-Human Behavior)
12. Application Of T-Pattern Analysis In The Study Of Rodent Behavior: Methodological And Experimental Highlights. Casarrubea M. et al
13. Using Hidden Behavioral Patterns To Study Nausea In A Preclinical Model. Horn C. and Magnusson M.S.
14. Informative Value Of Vocalizations During Multimodal Interactions In Red-Capped Mangabeys. Baraud I., et al.
15. Identification And Description Of Behaviours And Domination Patterns In Captive Vervet Monkeys (Cercophitecus Aethiops Pygerythrus) During Feeding Time. Ortiz G., et al.
16. Tidal Location Of Atlantic Cod In Icelandic Waters And Identification Of Vertical And Horizontal Movement Patterns In Cod Behavior. Jonsson G. et al.
17. Complex Spike Patterns in Olfactory Bulb Neuronal Networks.Nicol A. et al.
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