April 2012
Intermediate to advanced
384 pages
11h 19m
English
Content preview from Machine Learning in Action
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Chapter 6. Support vector machines
This chapter covers
- Introducing support vector machines
- Using the SMO algorithm for optimization
- Using kernels to “transform” data
- Comparing support vector machines with other classifiers
I’ve seen more than one book follow this pattern when discussing support vector machines (SVMs): “Here’s a little theory. Now SVMs are too hard for you. Just download libsvm and use that.” I’m not going to follow that pattern. I think if you just read a little bit of the theory and then look at production C++ SVM code, you’re going to have trouble understanding it. But if we strip out the production code and the speed improvements, the code becomes manageable, perhaps understandable.
Support vector ...
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