General Cost Functions for Support Vector Regression

General Cost Functions for Support Vector Regression (1998)

by Alex J. Smola , Bernhard Schölkopf , Klaus-Robert Müller

Venue:	IN PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS
Citations:	33 - 5 self

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Datum	Value	Source
TITLE	General Cost Functions for Support Vector Regression	user correction - Legacy Corrections
AUTHOR NAME	Alex J. Smola	user correction
AUTHOR ADDR	GMD FIRST, Rudower Chaussee 5, D--12489 Berlin	user correction
AUTHOR NAME	Bernhard Schölkopf	user correction
AUTHOR ADDR	GMD FIRST, Rudower Chaussee 5, D--12489 Berlin	user correction
AUTHOR NAME	Klaus-Robert Müller	user correction
AUTHOR ADDR	GMD FIRST, Rudower Chaussee 5, D--12489 Berlin	user correction
ABSTRACT	The concept of Support Vector Regression is extended to a more general class of convex cost functions. Moreover it is shown how the resulting convex constrained optimization problems can be efficiently solved by a Primal-Dual Interior Point path following method. Both computational feasibility and improvement of estimation is demonstrated in the experiments.	user correction
YEAR	1998	INFERENCE
VENUE	IN PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS	user correction
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	79--83	INFERENCE
CITATIONS	10 found	ParsCit 1.0