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



ID: 270016.0, MPI für biologische Kybernetik / Biologische Kybernetik
Breaking SVM Complexity with Cross-Training
Authors:Bakir, G.H.; Bottou, L.; Weston, J.
Editors:Saul, L.K.; Weiss, Y.; Bottou, L.
Date of Publication (YYYY-MM-DD):2005
Title of Proceedings:Advances in Neural Information Processing Systems
Start Page:81
End Page:88
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:We propose an algorithm for selectively removing examples from the training
set using probabilistic estimates related to editing algorithms
(Devijver and Kittler82). The procedure creates a separable distribution of
training examples with minimal impact on the decision boundary position. It
breaks the linear dependency between the number of SVs and the number of
training examples, and sharply reduces the complexity of SVMs
during both the training and prediction stages.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:2846
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