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Machine Learning: ECML 2004, Jean-Francois Boulicaut; Floriana Esposito; Fosca


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Автор: Jean-Francois Boulicaut; Floriana Esposito; Fosca
Название:  Machine Learning: ECML 2004
ISBN: 9783540231059
Издательство: Springer
Классификация:
ISBN-10: 3540231056
Обложка/Формат: Paperback
Страницы: 582
Вес: 0.83 кг.
Дата издания: 07.09.2004
Серия: Lecture Notes in Artificial Intelligence
Язык: English
Размер: 234 x 156 x 31
Основная тема: Computer Science
Подзаголовок: 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: The papers present a wealth of new results in the area and address all current issues in machine learning.


Practical Machine Learning with H2O

Автор: Darren Cook
Название: Practical Machine Learning with H2O
ISBN: 149196460X ISBN-13(EAN): 9781491964606
Издательство: Wiley
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Цена: 6334.00 р.
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Описание: This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.

Quantum Machine Learning: What Quantum Computing Means to Data Mining

Автор: Wittek Peter
Название: Quantum Machine Learning: What Quantum Computing Means to Data Mining
ISBN: 0128100400 ISBN-13(EAN): 9780128100400
Издательство: Elsevier Science
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Цена: 11789.00 р.
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Описание: Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. . Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications.

Machine Learning of Robot Assembly Plans

Автор: Alberto Maria Segre
Название: Machine Learning of Robot Assembly Plans
ISBN: 0898382696 ISBN-13(EAN): 9780898382693
Издательство: Springer
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Цена: 19564.00 р.
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Описание: This lack of unified purpose among the AI community makes this a very exciting time for AI research: new and diverse projects are springing up literally every day.

Machine Learning

Автор: Tom M. Mitchell; Jaime G. Carbonell; Ryszard S. Mi
Название: Machine Learning
ISBN: 0898382149 ISBN-13(EAN): 9780898382143
Издательство: Springer
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Цена: 23058.00 р.
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Описание: One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop.

Bayesian Reasoning and Machine Learning

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
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Цена: 11088.00 р.
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Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.

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>

Machine Learning

Автор: Mitchell
Название: Machine Learning
ISBN: 0071154671 ISBN-13(EAN): 9780071154673
Издательство: McGraw-Hill
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Цена: 10466.00 р.
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Описание: Covers the field of machine learning, which is the study of algorithms that allow computer programs to automatically improve through experience. This book is intended to support upper level undergraduate and introductory level graduate courses in machine learning.

Machine Learning for Hackers

Автор: Conway Drew, White John Myles
Название: Machine Learning for Hackers
ISBN: 1449303714 ISBN-13(EAN): 9781449303716
Издательство: Wiley
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Цена: 6334.00 р.
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Описание: Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data.

Machine Learning

Автор: Marsland
Название: Machine Learning
ISBN: 1466583282 ISBN-13(EAN): 9781466583283
Издательство: Taylor&Francis
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Цена: 12095.00 р.
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Описание:

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.

Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

New to the Second Edition

  • Two new chapters on deep belief networks and Gaussian processes
  • Reorganization of the chapters to make a more natural flow of content
  • Revision of the support vector machine material, including a simple implementation for experiments
  • New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
  • Additional discussions of the Kalman and particle filters
  • Improved code, including better use of naming conventions in Python

Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author's website.

Machine Learning: ECML-94

Автор: Francesco Bergadano; Luc de Raedt
Название: Machine Learning: ECML-94
ISBN: 3540578684 ISBN-13(EAN): 9783540578680
Издательство: Springer
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Цена: 12157.00 р.
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Описание: Machine learning, one of the most important research areas of artificial intelligence, is concerned with the automation of learning processes. This volume of conference proceedings contains the most significant research results in the field, describing techniques, implementations and experiments.

Machine Learning: ECML-98

Автор: Claire Nedellec; Celine Rouveirol
Название: Machine Learning: ECML-98
ISBN: 3540644172 ISBN-13(EAN): 9783540644170
Издательство: Springer
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Цена: 10480.00 р.
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Описание: The refereed proceedings of ECML-98, including 21 revised full papers and 25 short papers reporting on the work in progress together with two invited contributions. Applications of ML, inductive logic programming, relational learning, and instance-based learning are among the areas covered.

Machine Learning: ECML-95

Автор: Nada Lavra?; Stefan Wrobel
Название: Machine Learning: ECML-95
ISBN: 3540592865 ISBN-13(EAN): 9783540592860
Издательство: Springer
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Цена: 10480.00 р.
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Описание: These proceedings of the Eighth European Conference on Machine Learning, held in Heraclion, Crete in April 1995, address such areas as machine learning, logic programming, planning reasoning and algorithmic issues.


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