Active learning favoring points near the border between clusters

DOI

10.2991/isci-15.2015.110How to use a DOI?

Keywords

Active learning; SVM; support vector machine; k-medoids clustering

Abstract

An active learning SVM technique taking advantage of the cluster assumption was proposed. In each active learning iteration, unlabeled instances in the SVM margin were first grouped into two clusters. Then from each cluster, points most similar to the other cluster were selected for labeling. Such points lying near the border between clusters were expected to become support vectors with higher probability. The clustering process was performed in the same kernel space as SVM. With semi-supervised K-medoids, labeled instances were also used to improve the clustering performance. Experiments showed that the proposed method was efficient and robust (to poor initial samples).

Copyright

© 2015, 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  - Chunjiang Fu
AU  - Yupu Yang
PY  - 2015/01
DA  - 2015/01
TI  - Active learning favoring points near the border between clusters
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 831
EP  - 838
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.110
DO  - 10.2991/isci-15.2015.110
ID  - Fu2015/01
ER  -