For large training sets there is a guy called Bart Hamers in the ls-svm
area which is using approximation techniques for the kernel matrix.
May you check at:
For evaluation speed :
There exist some extension to the svm method, which keeps the nr.
of support vectors fixed. Than its acting similiar like a neural network.
I think details can be found in chapter ?10? ( I think) of "learning
with kernels" from Schoelkopf and Smola.
> > All,
> >I am surprised that very few people seem to use SVMs for commercial
> There are two important technical obstacles for SVM. One is to train SVM
> on a large dataset. The other is that testing speed is much slower than the
> previous methods such as neural networks. Recently I proposed a
> method to solve SVM's training on a very large dataset. For example,
> I trained ten SVMs on a dataset with 1,388,000 handwritten digit
> samples, 120 dimensional feature vectors on P4 1.7Ghz. The total time is
> just only about 6 hours. Also, I have designed a high-performance SVM
> package for research. For detail, visit the following website:
> For the testing problems, only Burges and Scholkopf reported some
> good experimental results on USPS and MNIST databases with the reduced
> set methods. The computational cost is very high, especially when the
> number of support vectors is more than ten thousand.
> For RBF kernel, this problem is related to a classical problem in applied
> mathematics: fast multipole method and fast gaussian transformation.
> Unfortunately both are effective only in R^2 and R^3. In a high dimensional space, the
> computational cost is prohibited.
> Therefore the development of a fast algorithm for SVM testing is necessary
> to enable it to be popular in commercial applications.
> Jianxiong Dong
> >Does this have to do with the patents covering SVMs? Do these
> >patents cover the application of SVMs to any problem domain? Are these
> >patents enforced?
> DONG JIANXIONG
> Centre for Pattern Recognition and Machine Intelligence
> GM606, Concordia University
> 1455 de Maisonneuve Blvd. West
> Montreal Quebec H3G 1M8, Canada
> Tel: (Office) (514)848-7953
> Fax: (514)848-4522
> E-mail: [log in to unmask]
> homepage: http://www.cenparmi.concordia.ca/~people/jdong