Advances in Learning Theory: Methods, Models, and Applications

Front Cover
Johan A. K. Suykens
IOS Press, 2003 - Computers - 415 pages
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, 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.
 

Contents

An Overview of Statistical Learning Theory
1
An Optimization Perspective on Kernel Partial Least Squares Regres
11
Cucker Smale Learning Theory in Besov Spaces
53
32
60
47
70
Functional Learning through Kernels
89
Leaveoneout Error and Stability of Learning Algorithms with
111
Regularized LeastSquares Classification
131
Bayesian Regression and Classification
267
from Likelihood Fields to Hyperfields
289
90
311
R Kulhavý
312
Bayesian Smoothing and Information Geometry
321
Györfi D Schäfer
332
Nonparametric Prediction
341
Recent Advances in Statistical Learning Theory
357

Least Squares Approaches and Extensions
155
Extension of the vSVM Range for Classification
179
Kernels Methods for Text Processing
197
Multiclass Learning with Output Codes
251
Neural Networks in Measurement Systems an engineering view
375
341
411
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