The book "Support Vector Machines for Pattern Classification" by Shigeo Abe, published by Springer, focuses on the application of support vector machines (SVMs) in pattern classification. The author, Professor Dr. Shigeo Abe from Kobe University, discusses the properties of SVMs that are useful for pattern classification, including multiclass models and variants of SVMs. The book compares the performance of these models using real-world benchmark data to illustrate their applicability. Key topics covered include: - Decision functions for two-class and multiclass problems - Architecture of SVMs for two-class classification, including hard-margin and soft-margin SVMs - Multiclass SVMs, such as one-against-all, pairwise, and all-at-once approaches - Variants of SVMs, including least squares, linear programming, and robust SVMs - Training methods to speed up SVM training, such as preselecting important data and decomposing the optimization problem - Feature selection and extraction techniques - Clustering using SVMs - Kernel-based methods for nonlinear separation - Maximum-margin approaches for multilayer neural networks and fuzzy classifiers - Function approximation using SVMs The book aims to provide a comprehensive guide for both theoretical understanding and practical implementation of SVMs in pattern recognition and classification tasks.The book "Support Vector Machines for Pattern Classification" by Shigeo Abe, published by Springer, focuses on the application of support vector machines (SVMs) in pattern classification. The author, Professor Dr. Shigeo Abe from Kobe University, discusses the properties of SVMs that are useful for pattern classification, including multiclass models and variants of SVMs. The book compares the performance of these models using real-world benchmark data to illustrate their applicability. Key topics covered include: - Decision functions for two-class and multiclass problems - Architecture of SVMs for two-class classification, including hard-margin and soft-margin SVMs - Multiclass SVMs, such as one-against-all, pairwise, and all-at-once approaches - Variants of SVMs, including least squares, linear programming, and robust SVMs - Training methods to speed up SVM training, such as preselecting important data and decomposing the optimization problem - Feature selection and extraction techniques - Clustering using SVMs - Kernel-based methods for nonlinear separation - Maximum-margin approaches for multilayer neural networks and fuzzy classifiers - Function approximation using SVMs The book aims to provide a comprehensive guide for both theoretical understanding and practical implementation of SVMs in pattern recognition and classification tasks.