An improved decomposition algorithm for regression Support Vector Machines

Advances in Neural Information Processing Systems

Volumn , Issue , 2000, Pages 484-490

  Laskov, Pavel ^a  

^a University of Delaware   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; OPTIMIZATION; PATTERN RECOGNITION; REGRESSION ANALYSIS;

ADVANCED SVM; BASIC PRINCIPLES; DECOMPOSITION ALGORITHM; DENSITY ESTIMATION; OPTIMALITY; REGRESSION SUPPORT VECTOR MACHINES; WORKING SET; WORKING SET SELECTION;

SUPPORT VECTOR MACHINES;

EID: 84898947865     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

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3. Joachims' T. (1998) Making Large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning. B. Scholköpf' C. Burges' A. Smola' (eds.) MIT-Press.
4. Osuna' E. (1998) Support Vector Machines: Training and Applications. Ph. D. Dissertation. Operations Research Center' MIT.
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7. Piatt' J. (1998) Fast Training of Support Vector Machines Using Sequential Minimal Optimization. Advances in Kernel Methods - Support Vector Learning. B. Scholköpf' C. Burges' A. Smola' (eds.) MIT-Press.
8. Kaufman' L. (1998) Solving the Quadratic Programming Problem Arising in Support Vector Classification. Advances in Kernel Methods - Support Vector Learning. B. Scholköpf' C. Burges' A. Smola' (eds.) MIT-Press.