NDLI: Laplacian regularized co-training

Content Provider	IEEE Xplore Digital Library
Author	Li, Yang Liu, Weifeng Wang, Yanjiang
Copyright Year	2014
Description	Author affiliation: College of Information and Control Engineering, China University of Petroleum (East China), #66 Changjiang West Road, Qingdao Economic & Technological Development Zone, 266580 China (Li, Yang; Liu, Weifeng; Wang, Yanjiang)
Abstract	Co-training is one promising paradigm of semi-supervised learning and has drawn considerable attentions and interests in recent years. It usually works in an iterative manner on two disjoint view features, in which two classifiers are trained on the different views and teach each other by adding the predictions of unlabeled data to the training set of the other view. However, the classifier performs not well with small number of labeled examples especially in the first rounds of interation. In this paper, we present Laplacian regularized co-training(LapCo) to address the above problem in standard co-training. During the training process, LapCo employs Laplacian regularization into the classifier to significantly boost the classification performance. The experiments on three popular UCI repository datasets are conducted and show that the proposed LapCo outperforms the traditional co-training method.
Starting Page	1408
Ending Page	1412
File Size	762914
Page Count	5
File Format	PDF
ISBN	9781479921881
ISSN	21645221
e-ISBN	9781479921867
DOI	10.1109/ICOSP.2014.7015231
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Classification algorithms Laplace equations Standards Support vector machines Partitioning algorithms Diabetes Laplacian regularization Semi-supervised learning co-traning
Content Type	Text
Resource Type	Article

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