Boosting Kernel Models for Regression

Authors:
Ping Sun;Xin Yao
Affiliations:
University of Birmingham, UK;University of Birmingham, UK
Venue:
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Year:
2006

Citing 0
Cited 3

Sparse approximation through boosting for learning large scale kernel machines

IEEE Transactions on Neural Networks
Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization

Genetic Programming and Evolvable Machines
Learning a lot from only a little: genetic programming for panel segmentation on sparse sensory evaluation data

EuroGP'10 Proceedings of the 13th European conference on Genetic Programming

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a general boosting framework for combining multiple kernel models in the context of both classification and regression problems. Our main approach is built on the idea of gradient boosting together with a new regularization scheme and aims at reducing the cubic com- plexity of training kernel models. We focus mainly on using the proposed boosting framework to combine kernel ridge regression (KRR) models for regression tasks. Numerical experiments on four large-scale data sets have shown that boosting multiple small KRR models is superior to training a single large KRR model on both improving generalization performance and reducing computational requirements.