PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combination of supervised and semi-supervised regression models for improved unbiased estimation
Jerónimo Arenas-Garcia, Carlos Moriana-Varo and Jan Larsen
In: IEEE International Symposium on Wireless Communication Systems, 19-22 Sep, 2010, York, UK.

Abstract

In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised and semisupervised methods, the resulting adaptive filter is guaranteed to perform at least as well as the best contributing filter, therefore achieving universal performance. The analysis and behavior of the methods is illustrated through a set of examples in a plant identification setup, analyzing both steady-state and convergence situations.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7547
Deposited By:Jerónimo Arenas-Garcia
Deposited On:17 March 2011