Active Learning Based on diversity maximization

DOI

10.2991/isccca.2013.207How to use a DOI?

Keywords

machine learning, active learning, classification , diversity

Abstract

In many practical data mining applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, as one type of the paradigms for addressing the problem of combining labeled and unlabeled data to boost the performance, active learning has attracted much attention. In this paper, we propose a new active learning approach based on diversity maximization. Different from the well-known co-testing algorithm, our method does not require two different views. The comparative studies with other active learning methods demonstrate the effectiveness of the proposed approach.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yongcheng Wu
PY  - 2013/02
DA  - 2013/02
TI  - Active Learning Based on diversity maximization
BT  - Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA 2013)
PB  - Atlantis Press
SP  - 822
EP  - 825
SN  - 1951-6851
UR  - https://doi.org/10.2991/isccca.2013.207
DO  - 10.2991/isccca.2013.207
ID  - Wu2013/02
ER  -