支持向量回归在线学习

Question

I am predicting stock prices using support vector regression. I have trained with some values but when i predict the values every time I have to train based on that(online learning). So i have passed the values to train inside the loop after predicted.

inside loop
 //prediction
  clf.fit(testx[i],testy[i])

So when i call the fit function everytime how svr training work internally based on one input ?

Answer 1

clf.fit is not incremental. You have to pass all the previous training points in addition to the new instance to re-train a new model that benefits from the new data points unfortunately.

This is a limitation of the SMO algorithm implemented by the libsvm library used internally in the sklearn.svm.SVR class.