Predictive Approaches for Sparse Model Learning

Shevade, SK and Sundararajan, S and Keerthi, SS (2004) Predictive Approaches for Sparse Model Learning. In: 11th International Conference on Neural Information Processing:ICONIP 2004(Lecture Notes in Computer Science), 22-25 November , 2004, Calcutta, India, Vol.3316, 434-439.

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Abstract

In this paper we investigate cross validation and Geisser’s sample reuse approaches for designing linear regression models. These approaches generate sparse models by optimizing multiple smoothing parameters. Within certain approximation, we establish equivalence relationships that exist among these approaches. The computational complexity, sparseness and performance on some benchmark data sets are compared with those obtained using relevance vector machine.

Item Type:	Conference Paper
Publication:	11th International Conference on Neural Information Processing, ICONIP 2004, Calcutta, India (Lecture Notes in Computer Science)
Publisher:	Springer Verlag
Additional Information:	Copyright of this article belongs to Springer Verlag.
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	09 May 2007
Last Modified:	19 Sep 2010 04:35
URI:	http://eprints.iisc.ac.in/id/eprint/10093

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