**Stop working (so hard) — I.M.H.O. — Medium**

"

'via Blog this'

**Stop working (so hard) — I.M.H.O. — Medium**

"

'via Blog this'

**3.2. Support Vector Machines — scikit-learn 0.13.1 documentation**

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**LIBSVM FAQ**

No, libsvm solves linear/nonlinear SVMs by the same way. Some tricks may save training/testing time if the linear kernel is used, so libsvm is NOT particularly efficient for linear SVM, especially when C is large and the number of data is much larger than the number of attributes. You can either

Use small C only. We have shown in the following paper that after C is larger than a certain threshold, the decision function is the same.

S. S. Keerthi and C.-J. Lin. Asymptotic behaviors of support vector machines with Gaussian kernel . Neural Computation, 15(2003), 1667-1689.

Check liblinear, which is designed for large-scale linear classification.

Please also see our SVM guide on the discussion of using RBF and linear kernels."

'via Blog this'

**Soft margin classification**

'via Blog this'

**Support vector machine - Wikipedia, the free encyclopedia**

'via Blog this'

**opencv - How to speed up svm.predict? - Stack Overflow**

'via Blog this'

**QuerySet API reference | Django documentation | Django**

'via Blog this'

**Support Vector Machines: Parameters**

Alpaydin (2004), page 224"

'via Blog this'

**New HTML Parser**: The long-awaited libxml2 based HTML parser
code is live. It needs further work but already handles most
markup better than the original parser.

Keep up with the latest Advogato features by reading the Advogato status blog.

If you're a C programmer with some spare time, take a look at the mod_virgule project page and help us with one of the tasks on the ToDo list!