PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Large Margin Hierarchical Classification
Ofer Dekel, Joseph Keshet and Yoram Singer
In: ICML 2004, 6-8 Jul 2004, Banff, Canada.

Abstract

We present an algorithmic framework for supervised classification learning where the set of labels is organized in a predefined hierarchical structure. This structure is encoded by a rooted tree which induces a metric over the label set. Our approach combines ideas from large margin kernel methods and Bayesian analysis. Following the large margin principle, we associate a prototype with each label in the tree and formulate the learning task as an optimization problem with varying margin constraints. In the spirit of Bayesian methods, we impose similarity requirements between the prototypes corresponding to adjacent labels in the hierarchy. We describe new online and batch algorithms for solving the constrained optimization problem. We derive a worst case loss-bound for the online algorithm and provide generalization analysis for its batch counterpart. We demonstrate the merits of our approach with a series of experiments on synthetic, text and speech data.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
Postscript - Requires a viewer, such as GhostView
EPrint Type:Conference or Workshop Item (Lecture)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Speech
Theory & Algorithms
ID Code:55
Deposited By:Joseph Keshet
Deposited On:20 May 2004