Extremely Randomized Trees

Extremely Randomized Trees (2003)

by Pierre Geurts

Venue:	MACHINE LEARNING
Citations:	88 - 30 self

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Datum	Value	Source
TITLE	Extremely Randomized Trees	user correction - Legacy Corrections
AUTHOR NAME	Pierre Geurts	user correction
AUTHOR AFFIL	University of Liege; Department of Electrical Engineering and Computer Science; Institut Monteore, Sart Tilman B28	user correction
AUTHOR ADDR	B-4000 Liege Belgium	user correction
ABSTRACT	This paper presents a new learning algorithm based on decision tree ensembles. In opposition to the classical decision tree induction method, the trees of the ensemble are built by selecting the tests during their induction fully at random. This extreme	user correction
YEAR	2003	user correction - Legacy Corrections
VENUE	MACHINE LEARNING	user correction
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	2006	INFERENCE
VOLUME	36	INFERENCE
TECH	Technical report	INFERENCE
CITATIONS	19 found	ParsCit 1.0