They are just different implementations of the same algorithm. The SVM module (SVC, NuSVC, etc) is a wrapper around the libsvm library and supports different kernels while LinearSVC is based on liblinear and only supports a linear kernel. So: SVC(kernel=”linear”) is in theory “equivalent” to: LinearSVC() Because the implementations are different in practice you will … Read more
The standard recommendation for a tutorial in SVMs is A Tutorial on Support Vector Machines for Pattern Recognition by Christopher Burges. Another good place to learn about SVMs is the Machine Learning Course at Stanford (SVMs are covered in lectures 6-8). Both these are quite theoretical and heavy on the maths. As for source code; … Read more
This is because of the differences between python 2 and python 3. Use the built-in function next in python 3. That is, write next(reader) instead of reader.next() in line 47. In addition, you should open the file in the text mode. So, change line 47 as i = open( input_file, ‘r’ ).
LibSVM uses the one-against-one approach for multi-class learning problems. From the FAQ: Q: What method does libsvm use for multi-class SVM ? Why don’t you use the “1-against-the rest” method ? It is one-against-one. We chose it after doing the following comparison: C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-class support vector … Read more
Support Vector Machines are an optimization problem. They are attempting to find a hyperplane that divides the two classes with the largest margin. The support vectors are the points which fall within this margin. It’s easiest to understand if you build it up from simple to more complex. Hard Margin Linear SVM In a training … Read more