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Constructing a speculative kernel machine for pattern classification
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DOI：10.1016/j.neunet.2005.06.051.png)
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Abstract
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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. (c) 2005 Elsevier Ltd. All rights reserved.
Keywords:
pattern recognition
classification
kernel machines
QR factorization
model selection
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