2015-04-30

One paper is accepted by Neurocomputing.

A General Framework for Co-Training and Its Applications

Abstract

Co-training is one of the major semi-supervised learning paradigms in which two classifiers are alternately trained on two distinct views and they teach each other by adding the predictions of unlabeled data to the training set of the other view. Co-training can achieve promising performance, especially when there is only a small number of labeled data. Hence, co-training has received considerable attention, and many variant co-training algorithms have been developed. It is essential and informative to provide a systematic framework for a better understanding of the common properties and differences in these algorithms. In this paper, we propose a general framework for co-training according to the diverse learners constructed in co-training. Specifically, we provide three types of co-training implementations, including co-training on multiple views, co-training on multiple classifiers, and co-training on multiple manifolds. Finally, comprehensive experiments of different methods are conducted on the UCF-iPhone dataset for human action recognition and the USAA dataset for social activity recognition. The experimental results demonstrate the effectiveness of the proposed solutions.

Key Words: Semi-supervised learning; Co-training; multi-view; human action recognition; social activity recognition;

2015-04-13

一研究生被推荐为山东省2015年省级优秀毕业研究生

课题组刘红丽被推荐为2015年省级优秀毕业生。


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经审查,学校96名毕业研究生符合省级优秀毕业生评选条件,96名毕业研究生符合校级优秀毕业生评选条件,自4月14日12:00至4月16日12:00予以公示。

http://ygb.upc.edu.cn/s/43/t/95/ae/3c/info44604.htm

2015-04-12

一研究生创新工程项目获批

研究生李阳申请的2015年研究生创新工程项目"基于协同训练的行为识别"获批公示中。