Kernel methods and the exponential family

ESANN 2005 Proceedings - 13th European Symposium on Artificial Neural Networks

Volumn , Issue , 2007, Pages 447-454

  Canu, Stéphane ^a   Smola, Alex J ^b  

^a UNIVERSITÉ ET INSA DE ROUEN   (France)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EXPONENTIAL FAMILY; EXPONENTIAL FAMILY MODELS; KERNEL METHODS; LEARNING MACHINES; NOVELTY DETECTION; ONE CLASS-SVM; REPRODUCING KERNEL HILBERT SPACES; STANDARD TOOLS;

LEARNING ALGORITHMS; NEURAL NETWORKS; TOOLS;

SUPPORT VECTOR MACHINES;

EID: 84887001725     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. S. Canu, X. Mary, and A. Rakotomamonjy. Advances in Learning Theory: Methods, Models and Applications NATO Science Series III: Computer and Systems Sciences, chapter Functional learning through kernel. IOS Press, Amsterdam, 2003.
2. A Smola. Exponential families and kernels. Berder summer school, 2004. http://users.rsise.anu.edu.au/smola/teaching/summer2004/.
3. Laurent Schwartz. Sous espaces hilbertiens d'espaces vectoriels topologiques et noyaux associés. Journal d'Analyse Mathématique, pages 115-256, 1964.
4. Grace Wahba. Spline Models for Observational Data. Series in Applied Mathematics, Vol. 59, SIAM, Philadelphia, 1990.
5. B. Scholkopf and A. Smola. Leaning with Kernels. MIT Press, 2001.
6. B Scholkopf, R Williamson, A Smola, and J Shawe-Taylor. Support vector method for novelty detection. In SA Solla, TK Leen, and KR Muller, editors, NIPS, pages 582-588. MIT Press, 2000.