14.2.16.1 Support Vector Machines, SVM, Surveys, Reviews, General

Chapter Contents (Back)
Support Vector Machines. SVM. Survey, SVM.

Cortes, C.[Corinna], Vapnik, V.[Vladimir],
Support-Vector Networks,
MachLearn(20), No. 3, 1995, pp. 273-297. Initial description for SVM ideas. 0906
BibRef

Vapnik, V.[Vladimir],
The Support Vector Method,
ICANN97(263-271). 0906
BibRef

Schölkopf, B.[Bernhard], Burges, C.[Chris], Vapnik, V.[Vladimir],
Incorporating Invariances in Support Vector Learning Machines,
ICANN96(47-52). 0906
BibRef

Chang, C.C., Lin, C.J.,
LIBSVM: a library for support vector machines,
Online2001.
WWW Version. Code, Support Vector Machines. BibRef 0100

LIBSVMTL: a Support Vector Machine Template Library,
Online2001.
HTML Version. Code, Support Vector Machines. Based on LIBSVM above. BibRef 0100

Schölkopf, B.[Bernhard],
Support Vector Machines,
Oldenbourg Verlag: Munich, 1997. BibRef 9700

Schölkopf, B.[Bernhard],
Support Vector Learning,
R. Oldenbourg VerlagMunich, 1997.
WWW Version. BibRef 9700

Scholkopf, B.[Bernhard], Smola, A.J.[Alexander J.], Muller, K.R.[Klaus-Robert], Bartlett, P.L.,
New Support Vector Algorithms,
NeurComp(12), 2000, pp. 1207-1245. BibRef 0001

Cristianini, N.[Nello], Schölkopf, B.[Bernhard],
Support Vector Machines and Kernel Methods: The New Generation of Learning Machines,
AIMag(23), No. 3, Fall 2002, pp. 31-41. Survey, SVM. Survey and general discussion. BibRef 0200

Kienzle, W.[Wolf], Bakir, G.H.[Gökhan H.], Franz, M.O.[Matthias O.], Schölkopf, B.[Bernhard],
Efficient Approximations for Support Vector Machines in Object Detection,
DAGM04(54-61).
WWW Version. 0505
BibRef

Cristianini, N.[Nello], Shawe-Taylor, J.[John],
An Introduction to Support Vector Machines,
Cambridge University Press2000. Survey, SVM.
WWW Version. ISBN: 0 521 78019 5 To purchase this book look here BibRef 0001

Chapelle, O., Haffner, P., Vapnik, V.,
Support Vector Machines for Histogram-Based Image Classification,
TNN(10), No. 5, May 1999, pp. 1055-1064. BibRef 9905


Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Robust Techniques, Robust Classification .


Last update:Nov 16, 2009 at 19:35:14