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Proceedings Paper

Relevance units machine based on Akaike's information criterion
Author(s): Jun Zhang; Junbin Gao
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Paper Abstract

The relevance vector machine (RVM) is a sparse regression kernel model. It not only generates a much sparser model but provides better generalization performance than the standard support vector machine (SVM). Relevance vectors and support vectors are both selected from the input vector set. This may limit model flexibility. Recently, we propose Relevance Units Machine (RUM). RUM treats relevance units (RUs) as part of the parameters of the model. However, the number of RUs must be selected before using RUM. In this paper, we use Akaike's Information Criterion (AIC) to select the number of the RUs. The experiment results show that based on AIC RUM maintains all the advantages of RVM and offers superior sparsity.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749624 (30 October 2009); doi: 10.1117/12.832314
Show Author Affiliations
Jun Zhang, Huazhong Univ. of Science and Technology (China)
Junbin Gao, Charles Sturt Univ. (Australia)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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