Kernel matrix completion by semidefinite programming

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

Volumn 2415 LNCS, Issue , 2002, Pages 694-699

  Graepel, Thore ^a  

^a ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX OPTIMIZATION; MATRIX ALGEBRA; NEURAL NETWORKS; SUPPORT VECTOR MACHINES; ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; OPTIMIZATION;

CLASSIFICATION TASKS; COMPLETION ALGORITHMS; CONVEX OPTIMISATION; KERNEL BASED LEARNING ALGORITHM; NUMERICAL EXPERIMENTS; POSITIVE SEMIDEFINITENESS; SEMI-DEFINITE PROGRAMMING; WEIGHTED COMPLETION;

LEARNING ALGORITHMS;

EID: 84886994574     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-46084-5_113     Document Type: Conference Paper

References (5)

1. D. Achlioptas, F. McSherry, and B. Schölkopf. Sampling techniques for kernel methods. In S. B. Thomas G. Dietterich and Z. Ghahramani, editors, Advances in Neural Information Processing Systems.
2. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, UK, 2000.
3. D. Haussler. Convolutional kernels on discrete structures. Technical Report UCSCCRL-99-10, Computer Science Department, University of California at Santa Cruz, 1999.
4. C. Johnson, B. Kroschel, and H. Wolkowicz. An interior-point method for approximate positive semidefinite completions. Comput. Optim. Appl., 9(2):175-190, 1998.
5. L. Vanderberghe and S. Boyd. Semidefinite programming. SIAM Rev., 38(1):49-95, 1996.