Support Vector Regression Machines

Support Vector Regression Machines (1996)

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by Harris Drucker , Chris J.C. Burges , Linda Kaufman , Chris J. C , Burges* Linda Kaufman , Alex Smola , Vladimir Vapnik

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Datum	Value	Source
TITLE	Support Vector Regression Machines	user correction - Legacy Corrections
AUTHOR NAME	Harris Drucker	user correction - Legacy Corrections
AUTHOR AFFIL	*Bell Labs and Monmouth University; Department of Electronic Engineering	user correction - Legacy Corrections
AUTHOR ADDR	West Long Branch, NJ 07764	user correction - Legacy Corrections
AUTHOR NAME	Chris J.C. Burges	user correction - Legacy Corrections
AUTHOR NAME	Linda Kaufman	user correction - Legacy Corrections
AUTHOR NAME	Chris J. C	user correction - Legacy Corrections
AUTHOR AFFIL	*Bell Labs and Monmouth University; Department of Electronic Engineering	user correction - Legacy Corrections
AUTHOR ADDR	West Long Branch, NJ 07764	user correction - Legacy Corrections
AUTHOR NAME	Burges* Linda Kaufman	user correction - Legacy Corrections
AUTHOR AFFIL	*Bell Labs and Monmouth University; Department of Electronic Engineering	user correction - Legacy Corrections
AUTHOR ADDR	West Long Branch, NJ 07764	user correction - Legacy Corrections
AUTHOR NAME	Alex Smola	user correction - Legacy Corrections
AUTHOR AFFIL	*Bell Labs and Monmouth University; Department of Electronic Engineering	user correction - Legacy Corrections
AUTHOR ADDR	West Long Branch, NJ 07764	user correction - Legacy Corrections
AUTHOR NAME	Vladimir Vapnik	user correction - Legacy Corrections
AUTHOR AFFIL	*Bell Labs and Monmouth University; Department of Electronic Engineering	user correction - Legacy Corrections
AUTHOR ADDR	West Long Branch, NJ 07764	user correction - Legacy Corrections
ABSTRACT	A new regression technique based on Vapnik's concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression trees and ridge regression done in feature space. On the basis of these experiments, it is expected that SVR will have advantages in high dimensionality space because SVR optimization does not depend on the dimensionality of the input space. 1. Introduction In the following, lower case bold characters represent vectors and upper case bold characters represent matrices. Superscript "t" represents the transpose of a vector. y represents either a vector (in bold) or a single observance of the dependent variable in the presence of noise. y (p) indicates a predicted value due to the input vector xx (p) not seen in the training set. Suppose we have an unknown function G(xx) (the "truth") which is a function of a vector xx (termed input space). The vector xx t = [x 1 , x 2 , ..., x d ] has ...	user correction - Legacy Corrections
YEAR	1996	user correction - Legacy Corrections
CITATIONS	4 found	ParsCit 1.0