SCOPUS 정보 검색 플랫폼

Proceedings - International Conference on Pattern Recognition

Volumn 16, Issue 3, 2002, Pages 331-334

KMOD - A two-parameter SVM kernel for pattern recognition

(3) Ayat, N E a,b Cheriet, M a Suen, C Y b

a ÉCOLE DE TECHNOLOGIE SUPÉRIEURE (Canada)

b Civil and Environmental Engineering (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PROCESSING; DATABASE SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION; BENCHMARKING; CHARACTER RECOGNITION; CLASSIFICATION (OF INFORMATION); SUPPORT VECTOR MACHINES;

HANDWRITTEN DIGITS; NEIGHBORHOOD INFORMATION; SMOOTHNESS FUNCTIONAL HYPOTHESIS;

PATTERN RECOGNITION;

BREAST CANCER; HANDWRITTEN DIGIT; KERNEL APPLICATIONS; NEIGHBORHOOD INFORMATION; NIST DATABASE; RBF KERNELS; STATE OF THE ART; TWO PARAMETER;

EID: 15944378365 PISSN: 10514651 EISSN: None Source Type: Conference Proceeding
DOI: None Document Type: Conference Paper

Times cited : (18)

References (8)

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- N.E. Ayat, M. Cheriet, and C.Y. Suen. Kmod- a new support vector machine kernel for pattern recognition. Application to digit image recognition. In ICDAR, pages 1215-1219, Seattle,USA, Sept. 2001.
- (2001) ICDAR , pp. 1215-1219
- Ayat, N.E.¹ Cheriet, M.² Suen, C.Y.³

2
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- Empirical error based optimization of SVM kernels. Application to digit image recognition
- Niagara-on-the-lake
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- Ayat, N.E.¹ Cheriet, M.² Suen, C.Y.³

3
- 0026966646
- A training algorithm for optimal margin classifiers
- Pittsburg
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- (1992) Fifth Annual Workshop on Computational Learning Theory
- Boser, B.¹ Guyon, I.² Vapnik, V.³

4
- 0002229304
- Pairwise classification and support vector machines
- B. Schlkopf, C. Burges, and A. Smola, editors, chapter 15
- U. Kreßel. Pairwise classification and support vector machines. In B. Schlkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, chapter 15, pages 255-268, 1999.
- (1999) Advances in Kernel Methods - Support Vector Learning , pp. 255-268
- Kreßel, U.¹

5
- 0342502195
- Soft margins for adaboost
- G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for adaboost. Machine Learning, 43(3):287-320, 2001.
- (2001) Machine Learning , vol.43 , Issue.3 , pp. 287-320
- Rätsch, G.¹ Onoda, T.² Müller, K.-R.³

6
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- PhD thesis, GMD First, Berlin, Germany
- A. Smola. Learning with Kernels. PhD thesis, GMD First, Berlin, Germany, 1998.
- (1998) Learning with Kernels
- Smola, A.¹

7
- 0003450542
- NY, USA
- V. Vapnik. The Nature of Statistical Learning Theory: NY, USA, 1995.
- (1995) The Nature of Statistical Learning Theory
- Vapnik, V.¹

8
- 0008201734
- Entropy numbers, operators and support vector kernels
- B. Schikopf, C. Burges, and A. Smola, editors, chapter 9
- R. C. Williamson, A. J. Smola, and B. Scholkopf. Entropy numbers, operators and support vector kernels. In B. Schikopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, chapter 9, pages 127-144, 1999.
- (1999) Advances in Kernel Methods - Support Vector Learning , pp. 127-144
- Williamson, R.C.¹ Smola, A.J.² Scholkopf, B.³

* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.