PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gradient Boosting for Kernelized Output Spaces
Pierre Geurts, Louis Wehenkel and Florence d'Alché-Buc
In: Proceedings of the 24th international conference on Machine learning ACM International Conference Proceeding Series , 227 . (2007) ACM , pp. 289-296. ISBN 1 59593 793 3

Abstract

A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting in a principled way to complex output spaces (images, text, graphs etc.) and can be applied to a general class of base learners working in kernelized output spaces. Empirical results are provided on three problems: a regression problem, an image completion task and a graph prediction problem. In these experiments, the framework is combined with tree-based base learners, which have interesting algorithmic properties. The results show that gradient boosting significantly improves these base learners and provides competitive results with other tree-based ensemble methods based on randomization.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3604
Deposited By:Florence d'Alché-Buc
Deposited On:13 February 2008