loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Thanh-Binh Le and Sang-Woon Kim

Affiliation: Myongji University, Korea, Republic of

Keyword(s): Semi-supervised MarginBoost, Incremental learning strategy, Dissimilarity-based classifications.

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

Abstract: The aim of this paper is to present an incremental learning strategy by which the classification accuracy of the semi-supervised MarginBoost (SSMB) algorithm (d’Alch ´ e Buc, 2002) can be improved. In SSMB, both a limited number of labeled and a multitude of unlabeled data are utilized to learn a classification model. However, it is also well known that the utilization of the unlabeled data is not always helpful for semi-supervised learning algorithms. To address this concern when dealing with SSMB, in this paper we study a means of selecting only “small” helpful portion of samples from the additional available data. More specifically, this is done by performing SSMB after incrementally reinforcing the given labeled training data with a part of strong unlabeled data; we train the classification model in an incremental fashion by employing a small amount of “strong” samples selected from the unlabeled data per iteration. The proposed scheme is evaluated with well-known benchmark datab ases, including some UCI data sets, in two approaches: dissimilarity-based classification (DBC) (Pekalska and Duin, 2005) as well as conventional feature-based classification. Our experimental results demonstrate that, compared to previous approaches, it achieves better classification accuracy results. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.206.160.129

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Le, T. and Kim, S. (2012). ON IMPROVING SEMI-SUPERVISED MARGINBOOST INCREMENTALLY USING STRONG UNLABELED DATA. In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM; ISBN 978-989-8425-98-0; ISSN 2184-4313, SciTePress, pages 265-268. DOI: 10.5220/0003721202650268

@conference{icpram12,
author={Thanh{-}Binh Le. and Sang{-}Woon Kim.},
title={ON IMPROVING SEMI-SUPERVISED MARGINBOOST INCREMENTALLY USING STRONG UNLABELED DATA},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM},
year={2012},
pages={265-268},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003721202650268},
isbn={978-989-8425-98-0},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM
TI - ON IMPROVING SEMI-SUPERVISED MARGINBOOST INCREMENTALLY USING STRONG UNLABELED DATA
SN - 978-989-8425-98-0
IS - 2184-4313
AU - Le, T.
AU - Kim, S.
PY - 2012
SP - 265
EP - 268
DO - 10.5220/0003721202650268
PB - SciTePress