Ebook: Advances in Learning Theory. Methods, Models and Applications

Издательство IOS Press, 2003, -433 pp.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, data mining and text mining, finance, fraud detection, measurement systems, 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. This can be seen in the analysis of micro-array 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 in a way that the data can become processed in a reliable way. The subjects in this publication are very interdisciplinary and relate to problems studied in neural networks, machine learning, mathematics and statistics.An Overview of Statistical Learning Theory
Best Choices for Regularization Parameters in Learning Theory: On the Bias-Variance Problem
Cucker Smale Learning Theory in Besov Spaces
High-dimensional Approximation by Neural Networks
Functional Learning through Kernels
Leave-one-out Error and Stability of Learning Algorithms with Applications
Regularized Least-Squares Classification
Support Vector Machines: Least Squares Approaches and Extensions
Extension of the ν-SVM Range for Classification
An Optimization Perspective on Kernel Partial Least Squares Regression
Multiclass Learning with Output Codes
Bayesian Regression and Classification
Bayesian Field Theory: from Likelihood Fields to Hyperfields
Bayesian Smoothing and Information Geometry
Nonparametric Prediction
Recent Advances in Statistical Learning Theory
Neural Networks in Measurement Systems (an engineering view)