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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 232614.0, MPI für biologische Kybernetik / Biologische Kybernetik
Semi-supervised kernel regression using whitened function classes
Authors:Franz, M.O.; Kwon, Y.; Rasmussen, C.E.; Schölkopf, B.
Editors:Rasmussen, C. E.; Bülthoff, H. H.; Giese, M. A.; Schölkopf, B.
Date of Publication (YYYY-MM-DD):2004
Title of Proceedings:Pattern Recognition, Proceedings of the 26th DAGM Symposium
Start Page:18
End Page:26
Physical Description:9
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:The use of non-orthonormal basis functions in ridge regression leads
to an often undesired non-isotropic prior in function space. In this
study, we investigate an alternative regularization technique that
results in an implicit whitening of the basis functions by penalizing
directions in function space with a large prior variance. The
regularization term is computed from unlabelled input data that
characterizes the input distribution. Tests on two datasets using
polynomial basis functions showed an improved average performance
compared to standard ridge regression.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:2638
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