Less is more: Active learning with support vector machines

Less is more: Active learning with support vector machines (2000)

by Greg Schohn , David Cohn

Citations:

161 - 1 self

Datum	Value	Source
TITLE	Less is more: Active learning with support vector machines	INFERENCE
AUTHOR NAME	Greg Schohn	SVM HeaderParse 0.2
AUTHOR NAME	David Cohn	SVM HeaderParse 0.2
AUTHOR AFFIL	Just Research	SVM HeaderParse 0.2
AUTHOR ADDR	4616 Henry Street, Pittsburgh, PA 15213 USA	SVM HeaderParse 0.2
ABSTRACT	We describe a simple active learning heuristic which greatly enhances the generalization behavior of support vector machines (SVMs) on several practical document classification tasks. We observe a number of benefits, the most surprising of which is that a SVM trained on a wellchosen subset of the available corpus frequently performs better than one trained on all available data. The heuristic for choosing this subset is simple to compute, and makes no use of information about the test set. Given that the training time of SVMs depends heavily on the training set size, our heuristic not only offers better performance with fewer data, it frequently does so in less time than the naive approach of training on all available data. 1.	SVM HeaderParse 0.2
YEAR	2000	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	839--846	INFERENCE
CITATIONS	19 found	ParsCit 1.0