Constructing a speculative kernel machine for pattern classification
Constructing a speculative kernel machine for pattern classification
We propose and investigate the performance of a new geometry-based algorithm designed to identify potentially informative data points for classification. An incremental QR update scheme is used to build a classifier using a subset of these points as radial basis function centers. The minimum descriptive length and the leave-one-out error criteria are employed for automatic model selection. The proposed scheme is shown to generate parsimonious models, which perform generalization comparable to the state-of-the-art support and relevance vector machines.
pattern recognition, classification, kernel machines, qr factorization, model selection
84-89
Choudhury, Arindam
defdc858-1c15-45b9-9bd6-642c5c706467
Nair, Prasanth B.
d4d61705-bc97-478e-9e11-bcef6683afe7
Keane, Andy J.
26d7fa33-5415-4910-89d8-fb3620413def
January 2006
Choudhury, Arindam
defdc858-1c15-45b9-9bd6-642c5c706467
Nair, Prasanth B.
d4d61705-bc97-478e-9e11-bcef6683afe7
Keane, Andy J.
26d7fa33-5415-4910-89d8-fb3620413def
Choudhury, Arindam, Nair, Prasanth B. and Keane, Andy J.
(2006)
Constructing a speculative kernel machine for pattern classification.
Neural Networks, 19 (1), .
(doi:10.1016/j.neunet.2005.06.051).
Abstract
We propose and investigate the performance of a new geometry-based algorithm designed to identify potentially informative data points for classification. An incremental QR update scheme is used to build a classifier using a subset of these points as radial basis function centers. The minimum descriptive length and the leave-one-out error criteria are employed for automatic model selection. The proposed scheme is shown to generate parsimonious models, which perform generalization comparable to the state-of-the-art support and relevance vector machines.
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Published date: January 2006
Keywords:
pattern recognition, classification, kernel machines, qr factorization, model selection
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Local EPrints ID: 23316
URI: http://eprints.soton.ac.uk/id/eprint/23316
PURE UUID: cf3c4e6e-952a-4218-8341-38e715ba9ed3
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Date deposited: 15 Mar 2006
Last modified: 16 Mar 2024 02:53
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Contributors
Author:
Arindam Choudhury
Author:
Prasanth B. Nair
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