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From Curve Fitting to Machine Learning, Achim Zielesny


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Автор: Achim Zielesny
Название:  From Curve Fitting to Machine Learning
ISBN: 9783319325446
Издательство: Springer
Классификация:




ISBN-10: 3319325442
Обложка/Формат: Hardcover
Страницы: 498
Вес: 0.89 кг.
Дата издания: 22.04.2016
Серия: Intelligent Systems Reference Library
Язык: English
Издание: 2nd ed. 2016
Иллюстрации: 200 illustrations, color; 143 illustrations, black and white; xv, 498 p. 343 illus., 200 illus. in color.
Размер: 234 x 156 x 29
Читательская аудитория: General (us: trade)
Основная тема: Computer Science
Подзаголовок: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Experimental data analysis is at the core of scientific inquiry, and computers have taken this function to a new level. This volume is an interactive guide to complex modern analytical processes from non-linear curve fitting to clustering and machine learning.


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

Shock Fitting

Автор: Marcello Onofri; Renato Paciorri
Название: Shock Fitting
ISBN: 3319684264 ISBN-13(EAN): 9783319684260
Издательство: Springer
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Цена: 19564.00 р.
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Описание: This work traces the evolution of shock-fitting methods, from the pioneering methods based on the structured grids (boundary and floating shock-fitting) to recent developments on unstructured grids, illustrating algorithmic details, significant applications and potential developments.

Beyond Two: Theory and Applications of Multiple-Valued Logic

Автор: Melvin Fitting; Ewa Orlowska
Название: Beyond Two: Theory and Applications of Multiple-Valued Logic
ISBN: 3790825220 ISBN-13(EAN): 9783790825220
Издательство: Springer
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Цена: 23058.00 р.
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Описание: This volume represents the state of the art for much current research in many-valued logics. Areas covered include: Algebras of multiple valued logics and their applications, proof theory and automated deduction in multiple valued logics, fuzzy logics and their applications, and multiple valued logics for control theory and rational belief.

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
Curves and Surfaces in Computer Aided Geometric Design

Автор: Fujio Yamaguchi
Название: Curves and Surfaces in Computer Aided Geometric Design
ISBN: 3642489540 ISBN-13(EAN): 9783642489549
Издательство: Springer
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Цена: 6986.00 р.
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Effective Computational Geometry for Curves and Surfaces

Автор: Jean-Daniel Boissonnat; Monique Teillaud
Название: Effective Computational Geometry for Curves and Surfaces
ISBN: 3642069878 ISBN-13(EAN): 9783642069871
Издательство: Springer
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Цена: 23058.00 р.
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Описание: This book covers combinatorial data structures and algorithms, algebraic issues in geometric computing, approximation of curves and surfaces, and computational topology. Coverage includes references to open source software and discussion of potential applications of the presented techniques.

Minimum Action Curves in Degenerate Finsler Metrics

Автор: Matthias Heymann
Название: Minimum Action Curves in Degenerate Finsler Metrics
ISBN: 3319177524 ISBN-13(EAN): 9783319177526
Издательство: Springer
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Цена: 4890.00 р.
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Описание:

Preface.- Acknowledgements.- Acronyms.- Part I: Results.- Introduction.- Geometric Action Functionals.- Existence of Minimum Action Curves.- Properties of Minimum Action Curves.- Conclusions.- Some Proofs and Remarks.- Part II: Proofs.- Finding Points with Local Minimizers.- Proof of Lemma 6.1.- Part III: Proof of a Technical Lemma.- Proof of Lemma 6.15: Main Arguments.- Proof of Lemma 6.15: Some Technical Details.- Glossary.- Index.- References.

Ensembles in Machine Learning Applications

Автор: Oleg Okun; Giorgio Valentini; Matteo Re
Название: Ensembles in Machine Learning Applications
ISBN: 3662507064 ISBN-13(EAN): 9783662507063
Издательство: Springer
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Цена: 16977.00 р.
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Описание: This book collects papers from the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA), held as part of the 2010 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.


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