Proceedings of the 2015 International Symposium on Computers & Informatics

Active learning favoring points near the border between clusters

Authors
Chunjiang Fu, Yupu Yang
Corresponding Author
Chunjiang Fu
Available Online January 2015.
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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Volume Title
Proceedings of the 2015 International Symposium on Computers & Informatics
Series
Advances in Computer Science Research
Publication Date
January 2015
ISBN
978-94-62520-56-1
ISSN
2352-538X
DOI
10.2991/isci-15.2015.110How to use a DOI?
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/).

Cite this article

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  -