Averaging of kernel functions

Neurocomputing

Volumn 112, Issue , 2013, Pages 19-25

  Belanche, Lluís A ^a   Tosi, Alessandra ^a  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

Author keywords

Averaging processes; Kernels; Support vector machines

Indexed keywords

ARITHMETIC MEAN; AVERAGING PROCESSES; FLEXIBLE LEARNING; KERNEL FUNCTION; KERNEL LEARNING; KERNEL OPTIMIZATIONS; KERNELS; MULTIPLE KERNEL LEARNING;

COMPUTER APPLICATIONS; NEURAL NETWORKS;

SUPPORT VECTOR MACHINES;

ARTICLE; CALCULATION; GEOMETRY; KERNEL METHOD; MATHEMATICAL COMPUTING; MULTIVARIATE ANALYSIS; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROCESS OPTIMIZATION;

EID: 84877353397     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.11.044     Document Type: Article

References (19)

1. Shawe-Taylor J., Cristianini N. Kernel Methods for Pattern Analysis 2004, Cambridge University Press.
2. Vapnik V.N. The Nature of Statical Learning Theory 1995, Springer, New York.
3. de Finetti B. Sul concetto di media. Giornale dell'Istituto Italiano degli Attuari 1931, II(3):369-396.
4. Kolmogorov A.N. Sur la notion de moyenne. Acad. Naz. Lincei Mem. Cl. Sci. His. Mat. Natur. Sez. 1930, 12:388-391.
5. Nagumo M. Über eine Klasse der Mittelwerte. Jpn. J. Math. 1930, VI(1):71-79.
6. Hille E. Methods in Classical and Functional Analysis 1971, Addison-Wesley.
7. Hardy G., Littlewood J.E., Pölya G. Inequalities 1952, Cambridge University Press.
8. FitzGerald C., Micchelli C., Pinkus A. Functions that preserve families of positive semidefinite matrices. Linear Algebra Appl. 1995, 221:83-102.
9. Berg C., Christensen J.P.R., Ressel P. Harmonic Analysis on Semigroups. Theory of Positive Definite and Related Functions 1984, Springer-Verlag.
10. Schölkopf B. Learning with Kernels 2001, John Wiley.
11. Ll. Belanche, A. Tosi, Averaging of kernel functions, in: European Symposium on Artificial Neural Networks (ESANN 2012), Bruges, Belgium, pp. 363-368.
12. F.R. Bach, G.R.G. Lanckriet, M.I. Jordan, Multiple kernel learning, conic duality, and the SMO algorithm, in: Proceedings of the 21th Annual International Conference on Machine Learning (ICML 2004), pp. 6-13.
13. R. Bhatia, H. Kosaki, Mean matrices and infinite divisibility, Indian Statistical Institute Report 2006/05.
14. Chapelle O., Vapnik V., Bousquet O., Mukherjee S. Choosing multiple parameters for support vector machines. Mach. Learn. 2002, 46(1):131-159.
15. Keerthi S., Sindhwani V., Chapelle O. An efficient method for gradient-based adaptation of hyperparameters in SVM models. Advances in Neural Information Processing Systems 2007, vol. 19. MIT Press.
16. Igel C., Glasmachers T., Mersch B., Pfeifer N., Meinicke P. Gradient-based optimization of kernel-target alignment for sequence kernels applied to bacterial gene start detection. EEE/ACM Trans. Comput. Biol. Bioinform. 2007, 4(2):216-226.
17. Glasmachers T., Igel C. Maximum likelihood model selection for 1-norm soft margin SVMs with multiple parameters. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32(8):1522-1528.
18. Gönen M., Alpaydin E. Multiple kernel learning algorithms. J. Mach. Learn. Res. 2011, 12:2211-2268.
19. M. Varma, B.R. Babu, More generality in efficient multiple kernel learning, in: Proceedings of the 26th Annual International Conference on Machine Learning (ICML 2009), pp. 1065-1072.