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Machine Learning in Action by Peter Harrington

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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 machines are considered ...

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