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Constructing a speculative kernel machine for pattern classification

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
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), 84-89. (doi:10.1016/j.neunet.2005.06.051).

Record type: Article

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

Identifiers

Local EPrints ID: 23316
URI: https://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: 17 Jul 2017 16:18

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Contributors

Author: Arindam Choudhury
Author: Prasanth B. Nair
Author: Andy J. Keane

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