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



ID: 232573.0, MPI für biologische Kybernetik / Biologische Kybernetik
Learning with Local and Global Consistency
Authors:Zhou, D.; Bousquet, O.; Lal, T.N.; Weston, J.; Schölkopf, B.
Editors:Thrun, S.; Saul, L.; Schölkopf, B.
Date of Publication (YYYY-MM-DD):2004
Title of Proceedings:Advances in Neural Information Processing Systems
Start Page:321
End Page:328
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:We consider the general problem of learning from labeled and
unlabeled data, which is often called semi-supervised learning or
transductive inference. A principled approach to semi-supervised
learning is to design a classifying function which is sufficiently
smooth with respect to the intrinsic structure collectively revealed
by known labeled and unlabeled points. We present a simple
algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a
number of classification problems and demonstrates effective use of
unlabeled data.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:2333
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