NDLI: Disagreement-Based Co-training

Content Provider	IEEE Xplore Digital Library
Author	Tanha, J. van Someren, M. Afsarmanesh, H.
Copyright Year	2011
Abstract	Recently, Semi-Supervised learning algorithms such as co-training are used in many domains. In co-training, two classifiers based on different subsets of the features or on different learning algorithms are trained in parallel and unlabeled data that are classified differently by the classifiers but for which one classifier has large confidence are labeled and used as training data for the other. In this paper, a new form of co-training, called Ensemble-Co-Training, is proposed that uses an ensemble of different learning algorithms. Based on a theorem by Angluin and Laird that relates noise in the data to the error of hypotheses learned from these data, we propose a criterion for finding a subset of high-confidence predictions and error rate for a classifier in each iteration of the training process. Experiments show that the new method in almost all domains gives better results than the state-of-the-art methods.
Starting Page	803
Ending Page	810
File Size	374753
Page Count	8
File Format	PDF
ISBN	9781457720680
ISSN	10823409
e-ISBN	9780769545967
DOI	10.1109/ICTAI.2011.126
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Error analysis Prediction algorithms Training data Decision trees Labeling Boosting Disagreement learning Semi-Supervised Learning (SSL) Co-training Self-training Ensemble Learning
Content Type	Text
Resource Type	Article

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