This book explains the principles that make support vector machines (SVMs) a successful modelling and prediction tool for a variety of applications. The authors present the basic ideas of SVMs together with the latest developments and current research questions in a unified style. They identify 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 their computational efficiency compared to several other methods. The book provides a unique in-depth treatment of both fundamental and recent material on SVMs that so far has been scattered in the literature. The book can thus serve as both a basis for graduate courses and an introduction for statisticians, mathematicians, and computer scientists. It further provides a valuable reference for researchers working in the field. Explains the principles that make support vector machines a successful modelling and prediction tool for a variety of applications Rigorous treatment of state-of-the-art results on support vector machines Suitable for both graduate students and researchers in statistical machine learning INDICE: Preface.- Introduction.- Loss functions and their risks.- Surrogate loss functions.- Kernels and reproducing kernel Hilbert spaces.- Infinite samples versions of support vector machines.- Basic statistical analysis of SVMs.- Advanced statistical analysis of SVMs.- Support vector machines for classification.- Support vector machines for regression.- Robustness.- Computational aspects.- Data mining.- Appendix.- Notation and symbols.- Abbreviations.- Author index.- Subject index.- References.
- ISBN: 978-0-387-77241-7
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 615
- Fecha Publicación: 01/07/2008
- Nº Volúmenes: 1
- Idioma: Inglés