Description
Dieses Buch widmet sich der statistischen Theorie des Lernens und der Verallgemeinerung – das heit, dem Problem der Auswahl der gewnschten Funktion auf der Basis empirischen Datenmaterials. Anwendung findet die Theorie auf vielen verschiedenen Gebieten – in neuronalen Netzwerken, Fuzzy-Logic-Systemen und knstlicher Intelligenz – beispielsweise in der Psychologie und der Informationswissenschaft. (8/98) Partial table of contents: THEORY OF LEARNING AND GENERALIZATION. Two Approaches to the Learning Problem. Estimation of the Probability Measure and Problem of Learning. Conditions for Consistency of Empirical Risk Minimization Principle. The Structural Risk Minimization Principle. Stochastic Ill-Posed Problems. SUPPORT VECTOR ESTIMATION OF FUNCTIONS. Perceptrons and Their Generalizations. SV Machines for Function Approximations, Regression Estimation, and Signal Processing. STATISTICAL FOUNDATION OF LEARNING THEORY. Necessary and Sufficient Conditions for Uniform Convergence of Frequencies to Their Probabilities. Necessary and Sufficient Conditions for Uniform One-Sided Convergence of Means to Their Expectations. Comments and Bibliographical Remarks. References. Index.
