Combination of supervised and semi-supervised regression models for improved unbiased estimation

Jeronimo Arenas-Garķa, Carlos Moriana-Varo, Jan Larsen

AbstractIn 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.
Keywordssemi-supervised learning
TypeConference paper [With referee]
ConferenceProceedings of the Seventh International Symposium on Wireless Communication Systems (ISWCS2010)
Year2010    Month September    pp. 364 - 368
PublisherIEEE Press
ISBN / ISSNDOI:10.1109/ISWCS.2010.5624325
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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