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Machine Learning: ECML`97, Maarten van Someren; Gerhard Widmer


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Автор: Maarten van Someren; Gerhard Widmer
Название:  Machine Learning: ECML`97
ISBN: 9783540628583
Издательство: Springer
Классификация:
ISBN-10: 3540628584
Обложка/Формат: Paperback
Страницы: 366
Вес: 0.53 кг.
Дата издания: 09.04.1997
Серия: Lecture Notes in Artificial Intelligence
Язык: English
Размер: 234 x 156 x 20
Основная тема: Computer Science
Подзаголовок: 9th European Conference on Machine Learning, Prague, Czech Republic, April 23 - 25, 1997, Proceedings
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This volume presents 26 revised full papers, an abstract paper and two papers corresponding to the invited talks as well as descriptions from four satellite workshops. The volume covers the whole spectrum of current machine learning issues.


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.

Knowledge Engineering, Machine Learning and Lattice Computing with Applications

Автор: Manuel Grana; Carlos Toro; Robert J. Howlett; Lakh
Название: Knowledge Engineering, Machine Learning and Lattice Computing with Applications
ISBN: 3642373429 ISBN-13(EAN): 9783642373428
Издательство: Springer
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Цена: 6429.00 р.
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Описание: This book constitutes the refereed proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2012, held in San Sebastian, Spain, in September 2012.

Machine Learning and Systems Engineering

Автор: Sio-Iong Ao; Burghard B. Rieger; Mahyar Amouzegar
Название: Machine Learning and Systems Engineering
ISBN: 9400733747 ISBN-13(EAN): 9789400733749
Издательство: Springer
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Цена: 28732.00 р.
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Описание: A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009).

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.

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.

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: The New AI

Автор: Alpaydin Ethem
Название: Machine Learning: The New AI
ISBN: 0262529513 ISBN-13(EAN): 9780262529518
Издательство: MIT Press
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Цена: 2700.00 р.
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Описание:

A concise overview of machine learning -- computer programs that learn from data -- which underlies applications that include recommendation systems, face recognition, and driverless cars.

Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning -- the foundation of efforts to process that data into knowledge -- has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.

Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.

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.

Genetic Algorithms for Machine Learning

Автор: John J. Grefenstette
Название: Genetic Algorithms for Machine Learning
ISBN: 0792394070 ISBN-13(EAN): 9780792394075
Издательство: Springer
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Цена: 27944.00 р.
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Описание: Features the articles that were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference.

Machine Learning,Algorithms And App

Автор: Mohammed
Название: Machine Learning,Algorithms And App
ISBN: 1498705383 ISBN-13(EAN): 9781498705387
Издательство: Taylor&Francis
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Цена: 12707.00 р.
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Описание: Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.

Machine Learning in Medicine - Cookbook

Автор: Cleophas
Название: Machine Learning in Medicine - Cookbook
ISBN: 3319041800 ISBN-13(EAN): 9783319041803
Издательство: Springer
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Цена: 6986.00 р.
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Описание: This timely guide explains how to derive the most benefit from automatically mining the vast, and growing, quantities of information in medical databases. It lays out the fruits of the authors` expertise in a condensed, easily consulted format.

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>


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