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Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation Cover

Efficient Decision Trees for Multi–Class Support Vector Machines Using Entropy and Generalization Error Estimation

International Journal of Applied Mathematics and Computer Science
Volume 28 (2018): Issue 4 (December 2018)
By: Pittipol Kantavat,  Boonserm Kijsirikul,  Patoomsiri Songsiri,  Ken-Ichi Fukui and  Masayuki Numao  
Open Access
|Jan 2019

Authors

Pittipol Kantavat

pittipol.k@chula.ac.th

Department of Computer Engineering Chulalongkorn University, Pathumwan,, Bangkok, Thailand

Boonserm Kijsirikul

boonserm.k@chula.ac.th

Department of Computer Engineering Chulalongkorn University, Pathumwan,, Bangkok, Thailand

Patoomsiri Songsiri

patoomsiri.s@chula.ac.th

Department of Computer Engineering Chulalongkorn University, Pathumwan,, Bangkok, Thailand

Ken-Ichi Fukui

fukui@ai.sanken.osaka-u.ac.jp

Institute of Scientific and Industrial Research Osaka University,, Osaka, Japan

Masayuki Numao

numao@ai.sanken.osaka-u.ac.jp

Institute of Scientific and Industrial Research Osaka University,, Osaka, Japan
DOI: https://doi.org/10.2478/amcs-2018-0054 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
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Language: English
Page range: 705 - 717
Submitted on: Oct 3, 2017
Accepted on: May 18, 2018
Published on: Jan 11, 2019
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
support vector machine,
multi-class classification,
generalization error,
entropy,
decision tree
Related subjects:
Mathematics,
Applied mathematics

© 2019 Pittipol Kantavat, Boonserm Kijsirikul, Patoomsiri Songsiri, Ken-Ichi Fukui, Masayuki Numao, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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