Support Vector Machines for Pattern Classification, Shigeo Abe
Автор: Duda, Richard O. Название: Pattern classification with computer manual, 2r.e. ISBN: 0471703508 ISBN-13(EAN): 9780471703501 Издательство: Wiley Рейтинг: Цена: 27712.00 р. Наличие на складе: Поставка под заказ.
Описание: The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
Автор: Rokach Lior Название: Pattern Classification Using Ensemble Methods ISBN: 9814271063 ISBN-13(EAN): 9789814271066 Издательство: World Scientific Publishing Рейтинг: Цена: 13464.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.
Автор: Riesen Kaspar & Bunke Horst Название: Graph Classification And Clustering Based On Vector Space Embedding ISBN: 9814304719 ISBN-13(EAN): 9789814304719 Издательство: World Scientific Publishing Рейтинг: Цена: 17424.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Focuses on a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. This title aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.
Описание: Provides scientific and engineering research findings and developments in the area of mobile robotics and associated support technologies. This book contains peer reviewed articles presented at the CLAWAR 2012 conference.
Описание: Provides advanced scientific and engineering research findings and developments in the area of mobile robotics and associated support technologies. This book contains peer reviewed articles presented at the CLAWAR 2011 conference.
Автор: Ingo Steinwart; Andreas Christmann Название: Support Vector Machines ISBN: 1489989633 ISBN-13(EAN): 9781489989635 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e?ciency compared with several other methods.
Автор: Murty Название: Support Vector Machines and Perceptrons ISBN: 3319410628 ISBN-13(EAN): 9783319410623 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>
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