Learning Theory and Generalization for Neural Networks and Other
Supervised Learning Techniques
This tutorial will provide an introduction to the theory of the
generalization performance of supervised learning techniques.
It will explain several key models and describe the main results
relating the generalization performance of a learning system to its
complexity. The discussion will concentrate on pattern classification
and real prediction problems, using neural networks as examples, but
these results are of considerable importance in understanding a much
broader variety of phenomena in machine learning.
The latter part of the tutorial will concentrate on recent advances
that exploit these results to provide new analyses of large margin
classifiers. Many pattern classifiers, such as neural networks and
support vector machines, and techniques for combining classifiers,
such as boosting and bagging, predict class labels by thresholding
real-valued functions, and tend to have a large margin between the
predicted value and an incorrect prediction. This part of the
tutorial will focus on large margin classifiers, presenting results
on the generalization performance of these classifiers, and explaining
why their size is not the most appropriate measure of their complexity.
Last update: Wed Nov 11 11:26:41 EST 1998