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Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability

Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
The paper introduces a construction algorithm for sparse kernel modelling using the leave-one-out test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic. An efficient subset model selection procedure is developed in the orthogonal forward regression framework by incrementally maximizing the model generalization capability to construct sparse models with good generalization properties. The proposed algorithm achieves a fully automated model construction without resort to any other validation data set for costly model evaluation.
0 9533890 6 5
251-256
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03

Chen, S. and Hong, X. (2003) Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability. 9th Annual Conference of Chinese Automation and Computing Society in UK, United Kingdom. pp. 251-256 .

Record type: Conference or Workshop Item (Other)

Abstract

The paper introduces a construction algorithm for sparse kernel modelling using the leave-one-out test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic. An efficient subset model selection procedure is developed in the orthogonal forward regression framework by incrementally maximizing the model generalization capability to construct sparse models with good generalization properties. The proposed algorithm achieves a fully automated model construction without resort to any other validation data set for costly model evaluation.

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More information

Published date: 2003
Additional Information: Event Dates: 20th September 2003
Venue - Dates: 9th Annual Conference of Chinese Automation and Computing Society in UK, United Kingdom, 2003-09-20
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 259254
URI: https://eprints.soton.ac.uk/id/eprint/259254
ISBN: 0 9533890 6 5
PURE UUID: 97466057-1860-4eb7-8fa0-b17ca01702bc

Catalogue record

Date deposited: 14 Apr 2004
Last modified: 18 Jul 2017 09:24

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