SVM can use other functions to make data linearly separable. SVM can give non linear, intricate decision boundaries. SVM Decision Boundary is a straight line for linear SVM. Apply linear . If it has 0% error, your data is linearly separable.
c parameter SVM controlls trade off between smooth decision boundary and classifying training points correctly (may not generalize well, get a smooth boundary or get more points classified correctly). Effects of C especially obvious in the RVF kernel. A large c means get more training points correctly. Larger c --> more intricate boundaries
Gamma defines how far the influence of a single training example reaches. If gamma has a low value each pointer has a far reach, if gamma has a high value each point has a closer reach. A high gamma value will make decision boundaries pay close attention to those points that are close, but ignore those that are far. High value of gamma could mean a very wiggly decision boundary.
A point close to the frontier can really have a lot of weight and pull the frontier close to itself. Versus a low gamma, means more points will have weights of influence on the frontier, so the frontier end up being smoother.
svm kernel http://scikit-learn.org/stable/modules/svm.html#svm-kernels
Use SVM for Stock finance https://en.wikipedia.org/wiki/Support_vector_machine
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