I am trying out using relevance vector machines, as they seem to give me better results than SVM. I am curious to know why the classification method has a 'set_max_iterations' method, while the regression method does not. Is this due to differences in the algorithm? Also, I am finding that the regression method takes much, much longer than the classification method for the same size of problem - is this normal?
Thanks
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The RVM method isn't the most numerically robust method in machine
learning. These are some of its issues. I would recommend using an SVM
instead with appropriate hyperparameter selection instead. In particular,
you can start with this: http://dlib.net/ml.html#auto_train_rbf_classifier.
That will train a RBF SVM and do all the hyperparameter selection
automatically. It should be much more reliable than the RVM.
If you want to do regression then try the krr_trainer or rr_trainer.
Hi
I am trying out using relevance vector machines, as they seem to give me better results than SVM. I am curious to know why the classification method has a 'set_max_iterations' method, while the regression method does not. Is this due to differences in the algorithm? Also, I am finding that the regression method takes much, much longer than the classification method for the same size of problem - is this normal?
Thanks
The RVM method isn't the most numerically robust method in machine
learning. These are some of its issues. I would recommend using an SVM
instead with appropriate hyperparameter selection instead. In particular,
you can start with this: http://dlib.net/ml.html#auto_train_rbf_classifier.
That will train a RBF SVM and do all the hyperparameter selection
automatically. It should be much more reliable than the RVM.
If you want to do regression then try the krr_trainer or rr_trainer.