Support-Vector Networks

Support-Vector Networks (1995)

by Corinna Cortes , Vladimir Vapnik

Venue:	Machine Learning
Citations:	1491 - 22 self

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Datum	Value	Source
TITLE	Support-Vector Networks	user correction - Legacy Corrections
AUTHOR NAME	Corinna Cortes	SVM HeaderParse 0.1
AUTHOR AFFIL	1	SVM HeaderParse 0.2
AUTHOR NAME	Vladimir Vapnik	SVM HeaderParse 0.1
AUTHOR AFFIL	2	SVM HeaderParse 0.2
AUTHOR ADDR	AT&T Labs-Research, USA	SVM HeaderParse 0.1
ABSTRACT	The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the supportvector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.	user correction - Legacy Corrections
YEAR	1995	INFERENCE
VENUE	Machine Learning	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	273--297	INFERENCE
VOLUME	20	INFERENCE
CITATIONS	14 found	ParsCit 1.0