IJPAM: Volume 46, No. 1 (2008)

GENERAL ALGORITHMIC FRAMEWORKS
FOR ONLINE PROBLEMS

Yair Censor$^1$, Simeon Reich$^2$, Alexander J. Zaslavski$^3$
$^{1}$Department of Mathematics
University of Haifa
Mt. Carmel, Haifa, 31905, ISRAEL
e-mail: yair@math.haifa.ac.il
$^{2,3}$Department of Mathematics
Technion - Israel Institute of Technology
Haifa, 32000, ISRAEL
$^2$e-mail: sreich@techunix.technion.ac.il
$^3$e-mail: ajzasl@techunix.technion.ac.il


Abstract.We study general algorithmic frameworks for online learning tasks. These include binary classification, regression, multiclass problems and cost-sensitive multiclass classification. The theorems that we present give loss bounds on the behavior of our algorithms which depend on general conditions on the iterative step sizes.

Received: February 21, 2007

AMS Subject Classification: 68Q20

Key Words and Phrases: online learning, general algorithms, classification, regression, multiclass, convex feasibility

Source: International Journal of Pure and Applied Mathematics
ISSN: 1311-8080
Year: 2008
Volume: 46
Issue: 1