Автор: Bishop Название: Pattern Recognition and Machine Learning ISBN: 0387310738 ISBN-13(EAN): 9780387310732 Издательство: Springer Рейтинг: Цена: 6634 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.A forthcoming companion volume will deal with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets and demonstration programs.Christopher Bishop is Assistant Director at Microsoft Research Cambridge, and also holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, and was recently elected Fellow of the Royal Academy of Engineering. The author's previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.Coming soon:*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)*For instructors, worked solutions to remaining exercises from the Springer web site*Lecture slides to accompany each chapter*Data sets available for download
Автор: Kevin Murphy Название: Machine Learning ISBN: 0262018020 ISBN-13(EAN): 9780262018029 Издательство: Wiley Рейтинг: Цена: 6793 р. Наличие на складе: Есть у поставщика Поставка под заказ.
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.
The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Автор: Barber Название: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Издательство: Cambridge Academ Рейтинг: Цена: 6348 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
Автор: Darren Cook Название: Practical Machine Learning with H2O ISBN: 149196460X ISBN-13(EAN): 9781491964606 Издательство: Wiley Рейтинг: Цена: 3343 р. Наличие на складе: Есть (1 шт.) Описание: This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.
Автор: Ao Название: Machine Learning and Systems Engineering ISBN: 9048194180 ISBN-13(EAN): 9789048194186 Издательство: Springer Рейтинг: Цена: 19224 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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). This title contains research articles written by prominent researchers participating in the conference.
Автор: Shukla Название: Real Life Applications of Soft Computing ISBN: 1439822875 ISBN-13(EAN): 9781439822876 Издательство: Taylor&Francis Рейтинг: Цена: 15539 р. Наличие на складе: Поставка под заказ.
Описание: The application of soft computing techniques in industrial control, security, data mining, software world, robotics, can be easily seen. This book uncovers such applications, explaining the underlying technology and its implementation. It demonstrates how they can be modeled, designed, and implemented.
Автор: Zhang Название: Machine Learning in Bioinformatics ISBN: 0470116625 ISBN-13(EAN): 9780470116623 Издательство: Wiley Рейтинг: Цена: 11913 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to anlayze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc., have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization. This book compiles recent approaches in machine learning, showing promise in addressing different complex bioinformatics applications, from prominent researchers in the field.
Описание: The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book identifies good practices for the use of learning strategies in DAR, and identifies DAR tasks that are more appropriate for these techniques.
Описание: The second edition of this bestseller provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. This edition contains a new chapter on Bayesian network classifiers and a new section on object-oriented Bayesian networks, along with new applications and case studies. It includes a new section that addresses foundational problems with causal discovery and Markov blanket discovery and a new section that covers methods of evaluating causal discovery programs. The book also offers more coverage on the uses of causal interventions to understand and reason with causal Bayesian networks. Supplemental materials are available on the book’s website.
Автор: Saitta Название: Phase Transitions in Machine Learning ISBN: 0521763916 ISBN-13(EAN): 9780521763912 Издательство: Cambridge Academ Рейтинг: Цена: 6973 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.
Автор: Rogers Simon Название: First Course in Machine Learning ISBN: 1439824142 ISBN-13(EAN): 9781439824146 Издательство: Taylor&Francis Рейтинг: Цена: 4179 р. Наличие на складе: Поставка под заказ.
Автор: Flach Название: Machine Learning ISBN: 1107422221 ISBN-13(EAN): 9781107422223 Издательство: Cambridge Academ Рейтинг: Цена: 4162 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
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