Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Petra Perner Название: Machine Learning and Data Mining in Pattern Recognition ISBN: 3642030696 ISBN-13(EAN): 9783642030697 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 6th International Conference MLDM 2009 Leipzig Germany July 2325 2009 Proceedings. .
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
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: Ratner Bruce Название: Statistical and Machine-Learning Data Mining ISBN: 1439860912 ISBN-13(EAN): 9781439860915 Издательство: Taylor&Francis Рейтинг: Цена: 9033.00 р. Наличие на складе: Нет в наличии.
Описание: Rev. ed. of: Statistical modeling and analysis for database marketing. c2003.
Автор: Daniel S. Yeung; Zhi-Qiang Liu; Xi-Zhao Wang; Hong Название: Advances in Machine Learning and Cybernetics ISBN: 3540335846 ISBN-13(EAN): 9783540335849 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the thoroughly refereed post-proceedings of the 4th International Conference on Machine Learning and Cybernetics, ICMLC 2005, held in Guangzhou, China in August 2005.
Автор: Zhi-Hua Zhou; Takashi Washio Название: Advances in Machine Learning ISBN: 3642052231 ISBN-13(EAN): 9783642052231 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Most submissions received four reviews, a few submissions received ?ve reviews, while only several submissions received three reviews.
Автор: Soh Название: Advances in Machine Learning and Signal Processing ISBN: 3319322125 ISBN-13(EAN): 9783319322124 Издательство: Springer Рейтинг: Цена: 28734.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Thisbook presents important research findings and recent innovations in the fieldof machine learning and signal processing. A wide range of topics relating to machinelearning and signal processing techniques and their applications are addressed inorder to provide both researchers and practitioners with a valuable resourcedocumenting the latest advances and trends. The book comprises a carefulselection of the papers submitted to the 2015 International Conference on MachineLearning and Signal Processing (MALSIP 2015), which was held on 15–17 December2015 in Ho Chi Minh City, Vietnam with the aim of offering researchers,academicians, and practitioners an ideal opportunity to disseminate theirfindings and achievements. All of the included contributions were chosen byexpert peer reviewers from across the world on the basis of their interest tothe community. In addition to presenting the latest in design, development, andresearch, the book provides access to numerous new algorithms for machinelearning and signal processing for engineering problems.
Автор: Boris Kryzhanovsky; Witali Dunin-Barkowski; Vladim Название: Advances in Neural Computation, Machine Learning, and Cognitive Research ISBN: 3319666037 ISBN-13(EAN): 9783319666037 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning.
Автор: Aboul Ella Hassanien; Diego Alberto Oliva Название: Advances in Soft Computing and Machine Learning in Image Processing ISBN: 3319637533 ISBN-13(EAN): 9783319637532 Издательство: Springer Рейтинг: Цена: 32142.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is a collection of the latest applications of methods from soft computing and machine learning in image processing. It explores different areas ranging from image segmentation to the object recognition using complex approaches, and includes the theory of the methodologies used to provide an overview of the application of these tools in image processing. The material has been compiled from a scientific perspective, and the book is primarily intended for undergraduate and postgraduate science, engineering, and computational mathematics students. It can also be used for courses on artificial intelligence, advanced image processing, and computational intelligence, and is a valuable resource for researchers in the evolutionary computation, artificial intelligence and image processing communities.
Автор: Clarke Название: Principles and Theory for Data Mining and Machine Learning ISBN: 0387981349 ISBN-13(EAN): 9780387981345 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Extensive treatment of the most up-to-date topicsProvides the theory and concepts behind popular and emerging methodsRange of topics drawn from Statistics, Computer Science, and Electrical Engineering
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