Sciweavers

Share
NIPS
2004

Instance-Specific Bayesian Model Averaging for Classification

9 years 3 months ago
Instance-Specific Bayesian Model Averaging for Classification
Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for learning instance-specific models from data that are optimized to predict well for a particular instance. Based on this framework, we present a lazy instance-specific algorithm called ISA that performs selective model averaging over a restricted class of Bayesian networks. On experimental evaluation, this algorithm shows superior performance over model selection. We intend to apply such instance-specific algorithms to improve the performance of patient-specific predictive models induced from medical data.
Shyam Visweswaran, Gregory F. Cooper
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NIPS
Authors Shyam Visweswaran, Gregory F. Cooper
Comments (0)
books