Springer Support Vector Machines for Pattern Classification

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.