Structured large margin learning

De Feng Wang; Daniel S. Yeung; Wing W.Y. Ng; Eric C.C. Tsang; Xi Zhao Wang

Structured large margin learning

De Feng Wang^*
, Daniel S. Yeung
, Wing W.Y. Ng
, Eric C.C. Tsang
, Xi Zhao Wang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This paper presents a new large margin learning approach, namely structured large margin machine (SLMM), which incorporates both merits of "structured" learning models and advantages of large margin learning schemes. The promising features of this model, such as enhanced generalization ability, scalability, extensibility, and noise tolerance, are demonstrated theoretically and empirically. SLMM is of theoretical importance because it is a generalization of learning models like SVM, MPM, LDA, and M⁴ etc. Moreover, it provides a novel insight into the study of learning methods and forms a foundation for conceiving other "structured" classifiers.

Original language	English
Title of host publication	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages	4242-4248
Number of pages	7
State	Published - 2005
Externally published	Yes
Event	International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005

Publication series

Name	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Conference

Conference	International Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/Territory	China
City	Guangzhou
Period	18/08/05 → 21/08/05

Keywords

Kernel space
SVM
Structured learning

Cite this

@inproceedings{ec0f2f9322e9444f8db9838d0936976f,

title = "Structured large margin learning",

abstract = "This paper presents a new large margin learning approach, namely structured large margin machine (SLMM), which incorporates both merits of {"}structured{"} learning models and advantages of large margin learning schemes. The promising features of this model, such as enhanced generalization ability, scalability, extensibility, and noise tolerance, are demonstrated theoretically and empirically. SLMM is of theoretical importance because it is a generalization of learning models like SVM, MPM, LDA, and M4 etc. Moreover, it provides a novel insight into the study of learning methods and forms a foundation for conceiving other {"}structured{"} classifiers.",

keywords = "Kernel space, SVM, Structured learning",

author = "Wang, \{De Feng\} and Yeung, \{Daniel S.\} and Ng, \{Wing W.Y.\} and Tsang, \{Eric C.C.\} and Wang, \{Xi Zhao\}",

year = "2005",

language = "英语",

isbn = "078039092X",

series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

pages = "4242--4248",

booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

note = "International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",

}

Structured large margin learning. / Wang, De Feng; Yeung, Daniel S.; Ng, Wing W.Y. et al.
2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005. p. 4242-4248 (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Structured large margin learning

AU - Wang, De Feng

AU - Yeung, Daniel S.

AU - Ng, Wing W.Y.

AU - Tsang, Eric C.C.

AU - Wang, Xi Zhao

PY - 2005

Y1 - 2005

N2 - This paper presents a new large margin learning approach, namely structured large margin machine (SLMM), which incorporates both merits of "structured" learning models and advantages of large margin learning schemes. The promising features of this model, such as enhanced generalization ability, scalability, extensibility, and noise tolerance, are demonstrated theoretically and empirically. SLMM is of theoretical importance because it is a generalization of learning models like SVM, MPM, LDA, and M4 etc. Moreover, it provides a novel insight into the study of learning methods and forms a foundation for conceiving other "structured" classifiers.

AB - This paper presents a new large margin learning approach, namely structured large margin machine (SLMM), which incorporates both merits of "structured" learning models and advantages of large margin learning schemes. The promising features of this model, such as enhanced generalization ability, scalability, extensibility, and noise tolerance, are demonstrated theoretically and empirically. SLMM is of theoretical importance because it is a generalization of learning models like SVM, MPM, LDA, and M4 etc. Moreover, it provides a novel insight into the study of learning methods and forms a foundation for conceiving other "structured" classifiers.

KW - Kernel space

KW - SVM

KW - Structured learning

UR - https://www.scopus.com/pages/publications/28444445968

M3 - 会议稿件

AN - SCOPUS:28444445968

SN - 078039092X

SN - 9780780390928

T3 - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

SP - 4242

EP - 4248

BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005

Y2 - 18 August 2005 through 21 August 2005

ER -

Structured large margin learning

Abstract

Publication series

Conference

Keywords

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