Advances in Learning Theory: Methods, Models and Applications: v. 190 - Rilegato

NATO Advanced Study Institute On Learning Theory And Practice (2002 : Louvain, Belgium); Suykens, Johan A. K.

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ISBN 10: 1586033417 ISBN 13: 9781586033415

Casa editrice: Ios Pr Inc, 2003

Riassunto

New methods, models, and applications in learning theory were the central themes of a NATO Advanced Study Institute held in July 2002. Contributors in neural networks, machine learning, mathematics, statistics, signal processing, and systems and control shed light on areas such as regularization parameters in learning theory, Cucker Smale learning theory in Besov spaces, high-dimensional approximation by neural networks, and functional learning through kernels. Other subjects discussed include leave-one-out error and stability of learning algorithms with applications, regularized least-squares classification, support vector machines, kernels methods for text processing, multiclass learning with output codes, Bayesian regression and classification, and nonparametric prediction. Annotation ©2004 Book News, Inc., Portland, OR (booknews.com)

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In recent years, considerable progress has been made in the understanding of problems of learning and generalization. In this context, Intelligence basically means the ability to perform well on new data after learning a model on the basis of given data. Such problems arise in many different areas and are becoming increasingly important and crucial towards many applications such as in bioinformatics, multimedia, computer vision and signal processing, internet search and information retrieval, datamining and textmining, finance, fraud detection, measurement systems and process control, and several others. Currently, the development of new technologies enables to generate massive amounts of data containing a wealth of information that remains to become explored. Often the dimensionality of the input spaces in these novel applications is huge. In the analysis of microarray data, for example, where expression levels of thousands of genes need to be analyzed given only a limited number of experiments. Without performing dimensionality reduction, the classical statistical paradigms show fundamental shortcomings at this point. Facing these new challenges, there is a need for new mathematical foundations and models such that the data can become processed in a reliable way. These subjects are very interdisciplinary and relate to problems studied in neural networks, machine learning, mathematics and statistics.

Su questa edizione

• Editore: Ios Pr Inc
• Data di pubblicazione: 2003
• ISBN 10: 1586033417
• ISBN 13: 9781586033415
• Rilegatura: Copertina rigida
• Numero di pagine: 415
• Redattore: Horvath Gabor, Basu Sanbar, Micchelli Charles, Vandewalle Joos, Suykens Johan A. K.