Pattern classification based on local learning

Jing Peng; Bir Bhanu

Pattern classification based on local learning

Computer Science

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

Abstract

Local learning methods approximate a target function (a posteriori probability) by partitioning the input space into a set of local regions, and modeling a simple input-output relationship in each one. In order for local learning to be effective for pattern classification in high dimensional settings, regions must be chosen judiciously to minimize bias. This paper presents a novel region partitioning criterion that attempts to minimize bias by capturing differential relevance in input variables in an efficient way. The efficacy of the method is validated using a variety of real and simulated data.

Original language	English
Title of host publication	Advances in Pattern Recognition - Joint IAPR International Workshops SSPR 1998 and SPR 1998, Proceedings
Editors	Adnan Amin, Dov Dori, Pavel Pudil, Herbert Freeman
Publisher	Springer Verlag
Pages	882-889
Number of pages	8
ISBN (Print)	3540648585, 9783540648581
State	Published - 1 Jan 1998
Event	7th Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in Pattern Recognition, SPR 1998 - Sydney, Australia Duration: 11 Aug 1998 → 13 Aug 1998

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1451
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	7th Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in Pattern Recognition, SPR 1998
Country	Australia
City	Sydney
Period	11/08/98 → 13/08/98

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Cite this

Peng, J., & Bhanu, B. (1998). Pattern classification based on local learning. In A. Amin, D. Dori, P. Pudil, & H. Freeman (Eds.), Advances in Pattern Recognition - Joint IAPR International Workshops SSPR 1998 and SPR 1998, Proceedings (pp. 882-889). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1451). Springer Verlag.

Peng, Jing ; Bhanu, Bir. / Pattern classification based on local learning. Advances in Pattern Recognition - Joint IAPR International Workshops SSPR 1998 and SPR 1998, Proceedings. editor / Adnan Amin ; Dov Dori ; Pavel Pudil ; Herbert Freeman. Springer Verlag, 1998. pp. 882-889 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Local learning methods approximate a target function (a posteriori probability) by partitioning the input space into a set of local regions, and modeling a simple input-output relationship in each one. In order for local learning to be effective for pattern classification in high dimensional settings, regions must be chosen judiciously to minimize bias. This paper presents a novel region partitioning criterion that attempts to minimize bias by capturing differential relevance in input variables in an efficient way. The efficacy of the method is validated using a variety of real and simulated data.",

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Peng, J & Bhanu, B 1998, Pattern classification based on local learning. in A Amin, D Dori, P Pudil & H Freeman (eds), Advances in Pattern Recognition - Joint IAPR International Workshops SSPR 1998 and SPR 1998, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1451, Springer Verlag, pp. 882-889, 7th Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in Pattern Recognition, SPR 1998, Sydney, Australia, 11/08/98.

Pattern classification based on local learning. / Peng, Jing; Bhanu, Bir.

Advances in Pattern Recognition - Joint IAPR International Workshops SSPR 1998 and SPR 1998, Proceedings. ed. / Adnan Amin; Dov Dori; Pavel Pudil; Herbert Freeman. Springer Verlag, 1998. p. 882-889 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1451).

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

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AB - Local learning methods approximate a target function (a posteriori probability) by partitioning the input space into a set of local regions, and modeling a simple input-output relationship in each one. In order for local learning to be effective for pattern classification in high dimensional settings, regions must be chosen judiciously to minimize bias. This paper presents a novel region partitioning criterion that attempts to minimize bias by capturing differential relevance in input variables in an efficient way. The efficacy of the method is validated using a variety of real and simulated data.

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SN - 9783540648581

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Peng J, Bhanu B. Pattern classification based on local learning. In Amin A, Dori D, Pudil P, Freeman H, editors, Advances in Pattern Recognition - Joint IAPR International Workshops SSPR 1998 and SPR 1998, Proceedings. Springer Verlag. 1998. p. 882-889. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).