Applications of Regularized Least Squares in Classification Problems
(invited lecture for ALT 2004)
Author: Nicolò Cesa-Bianchi
Affiliation: Dipartimento di Scienze dell'Informazione,
Università degli Studi di Milano,
Regularized least squares (RLS) algorithms, like ridge regression,
are well-known approaches for the solution of regression problems.
Recently, kernel-based RLS techniques have been used to learn
linear-threshold classifiers. In this talk we will present some
sparse incremental variants of RLS for classification.
We will discuss applications of these variants to hierarchical
classification and selective sampling problems, showing theoretical
and empirical performance bounds. In some cases, like selective
sampling, we will prove bounds in both the statistical learning
framework and in the game-theoretic framework.
©Copyright 2004 Author