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Advances in machine learning and data science, 


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Цена: 27950.00р.
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Название:  Advances in machine learning and data science
ISBN: 9789811085680
Издательство: Springer
Классификация:




ISBN-10: 9811085684
Обложка/Формат: Paperback
Страницы: 380
Вес: 0.55 кг.
Дата издания: 17.05.2018
Серия: Advances in intelligent systems and computing
Язык: English
Издание: 1st ed. 2018
Иллюстрации: 50 tables, color; 157 illustrations, black and white; xii, 380 p. 157 illus.
Размер: 234 x 156 x 21
Читательская аудитория: Professional & vocational
Подзаголовок: Recent achievements and research directives
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions.These days we find many computer programs that exhibit various useful learning methods and commercial applications. Goal of machine learning is to develop computer programs that can learn from experience. Machine learning involves knowledge from various disciplines like, statistics, information theory, artificial intelligence, computational complexity, cognitive science and biology. For problems like handwriting recognition, algorithms that are based on machine learning out perform all other approaches. Both machine learning and data science are interrelated. Data science is an umbrella term to be used for techniques that clean data and extract useful information from data. In field of data science, machine learning algorithms are used frequently to identify valuable knowledge from commercial databases containing records of different industries, financial transactions, medical records, etc. The main objective of this book is to provide an overview on latest advancements in the field of machine learning and data science, with solutions to problems in field of image, video, data and graph processing, pattern recognition, data structuring, data clustering, pattern mining, association rule based approaches, feature extraction techniques, neural networks, bio inspired learning and various machine learning algorithms.

Дополнительное описание: Preface.- About the Editors.- Table of Contents.- 38 Papers.- Author Index.



The Elements of Statistical Learning

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 10480.00 р.
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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.

Machine Learning and Data Mining in Pattern Recognition

Автор: Petra Perner
Название: Machine Learning and Data Mining in Pattern Recognition
ISBN: 3642030696 ISBN-13(EAN): 9783642030697
Издательство: Springer
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Цена: 18167.00 р.
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Описание: 6th International Conference MLDM 2009 Leipzig Germany July 2325 2009 Proceedings. .

Data Mining: Practical Machine Learning Tools and Techniques,

Автор: Ian H. Witten
Название: Data Mining: Practical Machine Learning Tools and Techniques,
ISBN: 0123748569 ISBN-13(EAN): 9780123748560
Издательство: Elsevier Science
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Цена: 8695.00 р.
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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, 4 ed.

Автор: Witten, Ian H.
Название: Data Mining. Practical Machine Learning Tools and Techniques, 4 ed.
ISBN: 0128042915 ISBN-13(EAN): 9780128042915
Издательство: Elsevier Science
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Цена: 9262.00 р.
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Описание:

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
Statistical and Machine-Learning Data Mining

Автор: Ratner Bruce
Название: Statistical and Machine-Learning Data Mining
ISBN: 1439860912 ISBN-13(EAN): 9781439860915
Издательство: Taylor&Francis
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Цена: 9033.00 р.
Наличие на складе: Нет в наличии.

Описание: Rev. ed. of: Statistical modeling and analysis for database marketing. c2003.

Advances in Machine Learning and Cybernetics

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
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Цена: 22359.00 р.
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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.

Advances in Machine Learning

Автор: Zhi-Hua Zhou; Takashi Washio
Название: Advances in Machine Learning
ISBN: 3642052231 ISBN-13(EAN): 9783642052231
Издательство: Springer
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Цена: 12577.00 р.
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Описание: Most submissions received four reviews, a few submissions received ?ve reviews, while only several submissions received three reviews.

Advances in Machine Learning and Signal Processing

Автор: Soh
Название: Advances in Machine Learning and Signal Processing
ISBN: 3319322125 ISBN-13(EAN): 9783319322124
Издательство: Springer
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Цена: 28734.00 р.
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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.

Advances in Neural Computation, Machine Learning, and Cognitive Research

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
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Цена: 22359.00 р.
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Описание: This book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning.

Advances in Soft Computing and Machine Learning in Image Processing

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
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Цена: 32142.00 р.
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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.

Principles and Theory for Data Mining and Machine Learning

Автор: Clarke
Название: Principles and Theory for Data Mining and Machine Learning
ISBN: 0387981349 ISBN-13(EAN): 9780387981345
Издательство: Springer
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Цена: 27950.00 р.
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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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