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

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 Buy this book: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods 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

Morra, J.H., Tu, Z., Apostolova, L.G., Green, A.E., Toga, A.W., Thompson, P.M.,
Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation,
MedImg(29), No. 1, January 2010, pp. 30-43.
IEEE DOI Link 1001
BibRef

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