Multiple Classifier Systems Analysis and Design: Hybrid Multi-class SVM Classifiers

This book describes the need to develop classifiers for multi-class problems which can provide better accuracy. SVMs deliver state-of-the-art performance in real-world multi-class classification applications such as text categorization, hand-written character recognition, image classification, biosequences analysis and intrusion detection. Their first beginning in the early 1990s lead to a recent explosion of applications and deepening theoretical analysis, that has now recognized Support Vector Machines as one of the standard tools for machine learning and data mining. The main goal of this book is to develop SVM classifiers for multi-class problems which can provide better accuracy. Students will find the book both stimulating and accessible, while researchers will be guided smoothly through the material required for a good grasp of the theory and application of these classifiers. The concepts are introduced gradually in accessible and self-contained stages, though in each stage the presentation is meticulous and thorough. Pointers to relevant literature ensure that it forms an ideal starting point for further study.