Campus Event Calendar
Event Entry
What and Who
Active Learning with Model Selection in Linear Regression
Masashi Sugiyama
Tokyo Institute of Technology
Talk
AG 1, AG 2, AG 4, AG 5, SWS, RG1, RG2
Expert Audience
English
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Date, Time and Location
Monday, 24 September 2007
11:30
60 Minutes
E1 4
433 (Rotunda 4th floor)
Saarbrücken
Abstract
Optimally designing the location of training input points (active
learning) and choosing the best model (model selection) are two important
components of supervised learning and have been studied extensively.
However, these two issues seem to have been investigated separately as two
independent problems. If training input points and models are simultaneously
optimized, the generalization performance would be further improved. We call
this problem active learning with model selection. In this talk, I
introduce a new approach called ensemble active learning. The proposed
approach compares favorably with alternative methods such as iteratively
performing active learning and model selection in a sequential manner.
learning) and choosing the best model (model selection) are two important
components of supervised learning and have been studied extensively.
However, these two issues seem to have been investigated separately as two
independent problems. If training input points and models are simultaneously
optimized, the generalization performance would be further improved. We call
this problem active learning with model selection. In this talk, I
introduce a new approach called ensemble active learning. The proposed
approach compares favorably with alternative methods such as iteratively
performing active learning and model selection in a sequential manner.
Contact
Ellen Fries
502
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logged in users only
References:
Sugiyama, M. & Rubens, N.
A Batch Ensemble Approach to Active Learning with Model Selection, Technical
Report TR07-0004, Department of Computer Science, Tokyo Institute of
Technology, Tokyo, Japan, 2007.
http://www.cs.titech.ac.jp/~tr/reports/2007/TR07-0004.pdf
Sugiyama, M.
Active learning in approximately linear regression based on conditional
expectation of generalization error.
Journal of Machine Learning Research, vol.7 (Jan), pp.141-166, 2006.
http://sugiyama-www.cs.titech.ac.jp/~sugi/2006/ALICE.pdf
Sugiyama, M. & Mueller, K.-R.
Input-dependent estimation of generalization error under covariate shift.
Statistics & Decisions, vol.23, no.4, pp.249-279, 2005.
http://sugiyama-www.cs.titech.ac.jp/~sugi/2005/IWSIC.pdf
Ellen Fries, 08/21/2007 11:14 -- Created document.