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Journal of Arti cial Intelligence Research 11 (1999) 335{360 Submitted 8/98; published 11/99 Committee-Based Sample Selection
 

Summary: Journal of Arti cial Intelligence Research 11 (1999) 335{360 Submitted 8/98; published 11/99
Committee-Based Sample Selection
For Probabilistic Classi ers
Shlomo Argamon-Engelson argamon@mail.jct.ac.il
Department of Computer Science
Jerusalem College of Technology, Machon Lev
P.O.B. 16031
Jerusalem 91160, Israel
Ido Dagan dagan@cs.biu.ac.il
Department of Mathematics and Computer Science
Bar-Ilan University
52900 Ramat Gan, Israel
Abstract
In many real-world learning tasks it is expensive to acquire a su cient number of labeled
examples for training. This paper investigates methods for reducing annotation cost by
sample selection. In this approach, during training the learning program examines many
unlabeled examples and selects for labeling only those that are most informative at each
stage. This avoids redundantly labeling examples that contribute little new information.
Our work follows on previous research on Query By Committee, and extends the
committee-based paradigm to the context of probabilistic classi cation. We describe a

  

Source: Argamon, Shlomo - Department of Computer Science, Illinois Institute of Technology

 

Collections: Computer Technologies and Information Sciences