SCOPUS 정보 검색 플랫폼

ESANN 2011 proceedings, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Volumn , Issue , 2010, Pages 327-332

Selecting from an infinite set of features in SVM

(3) Flamary, Rémi a Yger, Florian a Rakotomamonjy, Alain a

a UNIVERSITÉ DE ROUEN (France)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE-SET ALGORITHMS; BETTER PERFORMANCE; CONTINUOUS PARAMETERS; DECISION FUNCTIONS; FEATURE EXTRACTION METHODS; FEATURE PARAMETERS; SIGNAL CLASSIFICATION; TEXTURE RECOGNITION;

NEURAL NETWORKS;

FEATURE EXTRACTION;

EID: 84887046505 PISSN: None EISSN: None Source Type: Conference Proceeding
DOI: None Document Type: Conference Paper

Times cited : (4)

References (7)

1
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- Learning the kernel matrix with semi-definite programming
- G. Lanckriet, N. Cristianini, L. El Ghaoui, P. Bartlett, and M. Jordan. Learning the kernel matrix with semi-definite programming. Journal of Machine Learning Research, 5:27-72, 2004.
- (2004) Journal of Machine Learning Research , vol.5 , pp. 27-72
- Lanckriet, G.¹ Cristianini, N.² Ghaoui, L.E.³ Bartlett, P.⁴ Jordan, M.⁵

2
- 70450161376
- Let the kernel figure it out: Principled learning of preprocessing for kernel classifiers
- Peter Gehler and Sebastian Nowozin. Let the kernel figure it out: Principled learning of preprocessing for kernel classifiers. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009.
- (2009) Proceedings of the IEEE Computer Society Conference On Computer Vision and Pattern Recognition
- Gehler, P.¹ Nowozin, S.²

3
- 77952634397
- Enhanced local texture feature sets for face recognition under difficult lighting conditions
- X. Tan and B. Triggs. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Processing, 19(6):1635-1650, 2010.
- (2010) IEEE Trans. Image Processing , vol.19 , Issue.6 , pp. 1635-1650
- Tan, X.¹ Triggs, B.²

4
- 0002709342
- Feature selection via concave minimization and support vector machines
- Morgan Kaufmann
- P. Bradley and O. Mangasarian. Feature selection via concave minimization and support vector machines. pages 82-99. Proceedings of the fifthteen International Conference in Machine Learning, Morgan Kaufmann, 1998.
- (1998) Proceedings of the Fifthteen International Conference In Machine Learning , pp. 82-99
- Bradley, P.¹ Mangasarian, O.²

5
- 14344252374
- Multiple kernel learning, conic duality, and the SMO algorithm
- F. Bach, G. Lanckriet, and M. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In Proceedings of the 21st International Conference on Machine Learning, pages 41-48, 2004.
- (2004) Proceedings of the 21st International Conference On Machine Learning , pp. 41-48
- Bach, F.¹ Lanckriet, G.² Jordan, M.³

6
- 85014561619
- A fast iterative shrinkage-thresholding algorithm for linear inverse problems
- A. Beck and M. Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2:183-202, 2009.
- (2009) SIAM Journal On Imaging Sciences , vol.2 , pp. 183-202
- Beck, A.¹ Teboulle, M.²

7
- 79954523094
- Regularizing common spatial patterns to improve bci designs: Unified theory and new algorithms
- F. Lotte and C. Guan. Regularizing common spatial patterns to improve bci designs: Unified theory and new algorithms. IEEE Trans Biomed Eng, to appear, 2010.
- (2010) IEEE Trans Biomed Eng, to Appear
- Lotte, F.¹ Guan, C.²

* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.