£108.92

Springer Support Vector Machines for Pattern Classification

114 black & white illustrations, 114 col

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Description

Master the complexities of machine learning with this comprehensive guide on Support Vector Machines (SVMs) for pattern classification. Part of the Advances in Computer Vision and Pattern Recognition series, this book provides a deep look into how SVMs function within modern computational frameworks. It offers a rigorous performance comparison between various classifiers and regressors, making it an essential resource for those studying AI and pattern recognition. Readers will find practical architectures designed for multiclass classification and function approximation problems. The text moves beyond theory by providing clear evaluation criteria for both classifiers and regressors. By utilizing publicly available data sets, the book demonstrates real-world performance evaluation, ensuring you can apply these mathematical concepts to actual data. Whether you are looking to improve the generalization ability of neural networks or explore kernel methods, this Springer publication provides the technical foundation needed to advance your research in computer science.

Key Features

Provides a detailed clarification of the specific characteristics found in two-class SVMs to build a strong foundational understanding.

Discusses advanced kernel methods designed to improve the generalization ability of both fuzzy systems and neural networks.

Includes a wide range of illustrations and practical examples to help visualize complex pattern classification concepts.

Features performance evaluations conducted using publicly available data sets for realistic application testing.

Examines specialized topics including Mahalanobis kernels and empirical methods for improved classification accuracy.

Offers structured architectures specifically for solving multiclass classification and function approximation problems.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
29 March 2010
Listed Since
11 January 2010

Barcode

No barcode data available

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