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SVMs are very much used for regression as well: http://scikit-learn.org/stable/modules/svm.html#regression http://scikit-learn.org/stable/modules/svm.html#regr
by achompas 14y ago
SVMs are very much used for regression as well:
http://scikit-learn.org/stable/modules/svm.html#regression http://scikit-learn.org/stable/modules/svm.html#regression
Note: the scikit-learn implementation of SVMs is based on libsvm:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/ http://www.csie.ntu.edu.tw/~cjlin/libsvm/
- rm999 14y agoInteresting, a quick glance at a paper on SVRs indicate they kind of work in the opposite manner of a SVM - in an SVM you try to maximize the number of points far away from the separator (taking into account class), whereas in regression you are trying to minimize this. Do you have much background using them? I'm curious how they perform on real-world tasks.
- achompas 14y agoYeah, there's the SVR "pipe" concept, where you attempt to fit the margin s.t. points are close to it. It's a great alternate use of SVM's obj. function optimization. I haven't really used SVRs aside from some exploratory work, so I can't speak too much about them. But I know they exist!