Learning with kernels : support vector machines, regularization, optimization, and beyond / / Bernhard Schölkopf, Alexander J. Smola

• Record Nr.: UNINA9910780260003321
• Autore: Schölkopf Bernhard
• Pubbl/distr/stampa: Cambridge, Mass., : MIT Press, ©2002
• ISBN: 0-262-25693-2; 0-585-47759-0
• Descrizione fisica: 1 online resource (645 p.)
• Collana: Adaptive computation and machine learning
• Altri autori (Persone): SmolaAlexander J
• Disciplina: 006.3/1
• Soggetti: Machine learning; Algorithms; Kernel functions
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references (p. [591]-616) and index.
• Nota di contenuto: Contents; Series Foreword; Preface; 1 - A Tutorial Introduction; I - Concepts and Tools; 2 - Kernels; 3 - Risk and Loss Functions; 4 - Regularization; 5 - Elements of Statistical Learning Theory; 6 - Optimization; II - Support Vector Machines; 7 - Pattern Recognition; 8 - Single-Class Problems: Quantile Estimation and Novelty Detection; 9 - Regression Estimation; 10 - Implementation; 11 - Incorporating Invariances; 12 - Learning Theory Revisited; III - Kernel Methods; 13 - Designing Kernels; 14 - Kernel Feature Extraction; 15 - Kernel Fisher Discriminant; 16 - Bayesian Kernel Methods; 17 - Regularized Principal Manifolds18 - Pre-Images and Reduced Set Methods; A - Addenda; B - Mathematical Prerequisites; References; Index; Notation and Symbols
• Sommario/riassunto: In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, ; information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.