Combining labeled and unlabeled data with co-training

Combining labeled and unlabeled data with co-training (1998)

Cached

Download Links

by Avrim Blum , Tom Mitchell

Citations:

946 - 27 self

Versions

Version History

Metadata Version 2

User correction supplied by mph

Datum	Value	Source
TITLE	Combining labeled and unlabeled data with co-training	INFERENCE
AUTHOR NAME	Avrim Blum	user correction
AUTHOR AFFIL	School of Computer Science; Carnegie Mellon University	user correction
AUTHOR ADDR	Pittsburgh, PA 15213-3891	user correction
AUTHOR NAME	Tom Mitchell	user correction
AUTHOR AFFIL	School of Computer Science; Carnegie Mellon University	user correction
AUTHOR ADDR	Pittsburgh, PA 15213-3891	user correction
ABSTRACT	We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated by the task of learning to classify web pages. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks that point to that page. We assume that either view of the example would be su cient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment amuch smaller set of labeled examples. Speci cally, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each algorithm's predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PAC-style analysis for this setting, and, more broadly, a PAC-style framework for the general problem of learning from both labeled and unlabeled data. We also provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to signi cant improvement of hypotheses in practice. As part of our analysis, we provide new re-	user correction
YEAR	1998	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	92--100	INFERENCE
CITATIONS	17 found	ParsCit 1.0