Bayes Point Machines: Estimating the Bayes Point in Kernel Space

Bayes Point Machines: Estimating the Bayes Point in Kernel Space (1999)

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by Ralf Herbrich , Thore Graepel , Colin Campbell

Venue:	IJCAI WORKSHOP SVMS
Citations:	24 - 2 self

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Datum	Value	Source
TITLE	Bayes Point Machines: Estimating the Bayes Point in Kernel Space	user correction - Legacy Corrections
AUTHOR NAME	Ralf Herbrich	user correction
AUTHOR NAME	Thore Graepel	user correction
AUTHOR AFFIL	Computer Science Department; Technical University of Berlin	user correction
AUTHOR ADDR	10587 Berlin, Germany	user correction
AUTHOR NAME	Colin Campbell	user correction
AUTHOR AFFIL	Department of Engineering Mathematics; Bristol University	user correction
AUTHOR ADDR	Bristol BS8 1TR, United Kingdom	user correction
ABSTRACT	From a Bayesian perspective Support Vector Machines choose the hypothesis corresponding to the largest possible hypersphere that can be inscribed in version space, i.e. in the space of all consistent hypotheses given a training set. Those boundaries of version space which are tangent to the hypersphere define the support vectors. An alternative and potentially better approach is to construct the hypothesis using the whole of version space. This is achieved by using a Bayes Point Machine which finds the midpoint of the region of intersection of all hyperplanes bisecting version space into two halves of equal volume (the Bayes point). It is known that the center of mass of version space approximates the Bayes point [ Watkin, 1993 ] . We suggest estimating the center of mass by averaging over the trajectory of a billiard ball bouncing in version space. Experimental results are presented indicating that Bayes Point Machines consistently outperform Support Vector Machines.	user correction
YEAR	1999	INFERENCE
VENUE	IJCAI WORKSHOP SVMS	user correction
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	23--27	INFERENCE
CITATIONS	15 found	ParsCit 1.0