A Summary Of The Kernel Matrix, And How To Learn It Effectively Using Semidefinite Programming

A. Aizerman, E. M. Braverman, and L. Rozoner, “Theoretical foundations of the potential function method in pattern recognition learning,” Automation and remote control , vol. 25, pp. 821–837, 1964

1964

Earlier work this paper cites.

Y. Nesterov and A. Nemirovskii, Interior-point polynomial algorithms in convex programming . SIAM, 1994

1994

Earlier work this paper cites.

C. Cortes and V. Vapnik, “Support-vector networks,” Machine learning , vol. 20, no. 3, pp. 273–297, 1995

1995

Earlier work this paper cites.

L. Vandenberghe and S. Boyd, “Semidefinite programming,” SIAM review , vol. 38, no. 1, pp. 49–95, 1996

1996

Earlier work this paper cites.

C. Saunders, A. Gammerman, and V. Vovk, “Ridge regression learning algorithm in dual variables,” in (ICML-1998) Proceedings of the 15th International Conference on Machine Learning . Morgan Kaufmann, 1998, pp. 515–521

1998

Earlier work this paper cites.

A. Gammerman, V. Vovk, and V. Vapnik, “Learning by transduction,” in Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence . Morgan Kaufmann Publishers Inc., 1998, pp. 148–155

1998

Cited alongside, same era.

J. F. Sturm, “Using sedumi 1.02, a matlab toolbox for optimization over symmetric cones,” Optimization methods and software , vol. 11, no. 1-4, pp. 625–653, 1999

1999

Cited alongside, same era.

N. Christianini and J. Shawe-Taylor, “Support vector machines,” Cambridge, UK: Cambridge University Press , vol. 93, no. 443, pp. 935–948, 2000

2000

Cited alongside, same era.

K. P. Bennett and E. J. Bredensteiner, “Duality and geometry in svm classifiers,” in ICML , 2000, pp. 57–64

2000

Cited alongside, same era.

E. D. Andersen and K. D. Andersen, “The mosek interior point optimizer for linear programming: an implementation of the homogeneous algorithm,” in High performance optimization . Springer, 2000, pp. 197–232

B. Schölkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond . MIT press, 2002

2002

Later among the works it cites.

T. De Bie, G. R. Lanckriet, and N. Cristianini, “Convex tuning of the soft margin parameter,” in Internal Report 04-127, ESAT-SISTA, KU Leuven . Citeseer, 2003

Original

2003

Later among the works it cites.

G. R. Lanckriet, N. Cristianini, P. Bartlett, L. E. Ghaoui, and M. I. Jordan, “Learning the kernel matrix with semidefinite programming,” Journal of Machine learning research , vol. 5, no. Jan, pp. 27–72, 2004

2004

Later among the works it cites.

J. Shawe-Taylor and N. Cristianini, Kernel methods for pattern analysis . Cambridge university press, 2004

2004

Later among the works it cites.

S. Boyd and L. Vandenberghe, Convex optimization . Cambridge university press, 2004

2004