On Hadamard-Type Output Coding in Multiclass Learning

Aijun Zhang; Zhi Li Wu; Chun Hung Li; Kai Tai Fang

doi:10.1007/978-3-540-45080-1_51

On Hadamard-Type Output Coding in Multiclass Learning

Aijun Zhang^*, Zhi Li Wu, Chun Hung Li, Kai Tai Fang

^*Corresponding author for this work

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

13 Citations (Scopus)

Abstract

The error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, combining an economical number of base learners. Under the criteria of row separation and column diversity, we suggest the use of Hadamard matrices to design output codes and show them better than other codes of the same size. Comparative experiments based on the support vector machines are made for some real datasets from the UCI machine learning repository.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning
Subtitle of host publication	4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers
Editors	Jiming Liu, Yiu-ming Cheung, Hujun Yin
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	397-404
Number of pages	8
Edition	1st
ISBN (Electronic)	9783540450801
ISBN (Print)	9783540405504
DOIs	https://doi.org/10.1007/978-3-540-45080-1_51
Publication status	Published - 29 Jul 2003
Event	4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003 - Hong Kong Convention and Exhibition Centre, Hong Kong, China Duration: 21 Mar 2003 → 23 Mar 2003 https://link.springer.com/book/10.1007/b11717 http://www.comp.hkbu.edu.hk/IDEAL2003/ (conference website) http://www.comp.hkbu.edu.hk/IDEAL2003/ (conference program)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Verlag
Volume	2690
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	IDEAL: International Conference on Intelligent Data Engineering and Automated Learning

Conference

Conference	4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003
Country/Territory	China
City	Hong Kong
Period	21/03/03 → 23/03/03
Internet address	https://link.springer.com/book/10.1007/b11717 http://www.comp.hkbu.edu.hk/IDEAL2003/ (conference website) http://www.comp.hkbu.edu.hk/IDEAL2003/ (conference program)

User-Defined Keywords

Error-correcting output codes
Hadamard matrix
Multiclass learning
Support vector machines

Access to Document

10.1007/978-3-540-45080-1_51

Cite this

Zhang, A., Wu, Z. L., Li, C. H., & Fang, K. T. (2003). On Hadamard-Type Output Coding in Multiclass Learning. In J. Liu, Y. Cheung, & H. Yin (Eds.), Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers (1st ed., pp. 397-404). (Lecture Notes in Computer Science; Vol. 2690), (IDEAL: International Conference on Intelligent Data Engineering and Automated Learning). Springer. https://doi.org/10.1007/978-3-540-45080-1_51

Zhang, Aijun ; Wu, Zhi Li ; Li, Chun Hung et al. / On Hadamard-Type Output Coding in Multiclass Learning. Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers. editor / Jiming Liu ; Yiu-ming Cheung ; Hujun Yin. 1st. ed. Berlin, Heidelberg : Springer, 2003. pp. 397-404 (Lecture Notes in Computer Science). (IDEAL: International Conference on Intelligent Data Engineering and Automated Learning).

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title = "On Hadamard-Type Output Coding in Multiclass Learning",

abstract = "The error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, combining an economical number of base learners. Under the criteria of row separation and column diversity, we suggest the use of Hadamard matrices to design output codes and show them better than other codes of the same size. Comparative experiments based on the support vector machines are made for some real datasets from the UCI machine learning repository.",

keywords = "Error-correcting output codes, Hadamard matrix, Multiclass learning, Support vector machines",

author = "Aijun Zhang and Wu, {Zhi Li} and Li, {Chun Hung} and Fang, {Kai Tai}",

note = "Copyright: Copyright 2008 Elsevier B.V., All rights reserved.; 4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003 ; Conference date: 21-03-2003 Through 23-03-2003",

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doi = "10.1007/978-3-540-45080-1_51",

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editor = "Jiming Liu and Yiu-ming Cheung and Hujun Yin",

booktitle = "Intelligent Data Engineering and Automated Learning",

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Zhang, A, Wu, ZL, Li, CH & Fang, KT 2003, On Hadamard-Type Output Coding in Multiclass Learning. in J Liu, Y Cheung & H Yin (eds), Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers. 1st edn, Lecture Notes in Computer Science, vol. 2690, IDEAL: International Conference on Intelligent Data Engineering and Automated Learning, Springer, Berlin, Heidelberg, pp. 397-404, 4th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2003, Hong Kong, China, 21/03/03. https://doi.org/10.1007/978-3-540-45080-1_51

On Hadamard-Type Output Coding in Multiclass Learning. / Zhang, Aijun; Wu, Zhi Li; Li, Chun Hung et al.
Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers. ed. / Jiming Liu; Yiu-ming Cheung; Hujun Yin. 1st. ed. Berlin, Heidelberg: Springer, 2003. p. 397-404 (Lecture Notes in Computer Science; Vol. 2690), (IDEAL: International Conference on Intelligent Data Engineering and Automated Learning).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Fang, Kai Tai

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N2 - The error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, combining an economical number of base learners. Under the criteria of row separation and column diversity, we suggest the use of Hadamard matrices to design output codes and show them better than other codes of the same size. Comparative experiments based on the support vector machines are made for some real datasets from the UCI machine learning repository.

AB - The error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, combining an economical number of base learners. Under the criteria of row separation and column diversity, we suggest the use of Hadamard matrices to design output codes and show them better than other codes of the same size. Comparative experiments based on the support vector machines are made for some real datasets from the UCI machine learning repository.

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Zhang A, Wu ZL, Li CH, Fang KT. On Hadamard-Type Output Coding in Multiclass Learning. In Liu J, Cheung Y, Yin H, editors, Intelligent Data Engineering and Automated Learning: 4th International Conference, IDEAL 2003 Hong Kong, China, March 21–23, 2003 Revised Papers. 1st ed. Berlin, Heidelberg: Springer. 2003. p. 397-404. (Lecture Notes in Computer Science). (IDEAL: International Conference on Intelligent Data Engineering and Automated Learning). doi: 10.1007/978-3-540-45080-1_51

On Hadamard-Type Output Coding in Multiclass Learning

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