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Authors: Thanh-Binh Le and Sang-Woon Kim

Affiliation: Myongji University, Korea, Republic of

ISBN: 978-989-758-076-5

Keyword(s): Semi-supervised Learning, Selecting Unlabeled Data, Multi-view Learning Techniques.

Related Ontology Subjects/Areas/Topics: Classification ; Pattern Recognition ; Theory and Methods

Abstract: In a semi-supervised learning approach, using a selection strategy, strongly discriminative examples are first selected from unlabeled data and then, together with labeled data, utilized for training a (supervised) classifier. This paper investigates a new selection strategy for the case when the data are composed of different multiple views: first, multiple views of the data are derived independently; second, each of the views are used for measuring corresponding confidences with which examples to be selected are evaluated; third, all the confidence levels measured from the multiple views are used as a weighted average for deriving a target confidence; this selecting-and-training is repeated for a predefined number of iterations. The experimental results, obtained using synthetic and real-life benchmark data, demonstrate that the proposed mechanism can compensate for the shortcomings of the traditional strategies. In particular, the results demonstrate that when the data is appropria tely decomposed into multiple views, the strategy can achieve further improved results in terms of the classification accuracy. (More)

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Paper citation in several formats:
Le, T. and Kim, S. (2015). On Selecting Useful Unlabeled Data Using Multi-view Learning Techniques.In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-076-5, pages 157-164. DOI: 10.5220/0005171301570164

@conference{icpram15,
author={Thanh{-}Binh Le. and Sang{-}Woon Kim.},
title={On Selecting Useful Unlabeled Data Using Multi-view Learning Techniques},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2015},
pages={157-164},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005171301570164},
isbn={978-989-758-076-5},
}

TY - CONF

JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - On Selecting Useful Unlabeled Data Using Multi-view Learning Techniques
SN - 978-989-758-076-5
AU - Le, T.
AU - Kim, S.
PY - 2015
SP - 157
EP - 164
DO - 10.5220/0005171301570164

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