Menu
I am using a multi-dimensional SVM classifier (SVM.NET, a wrapper for libSVM) to classify a set of features.
Given an SVM model, is it possible to incorporate new training data without having to recalculate on all previous data? I guess another way of putting it would be: is an SVM mutable?
693
Let's now build our SVM model using the numpy library. α(0.0001) is the learning rate and the regularization parameter λ is set to 1/epochs. Therefore, the regularizing value reduces the number of epochs increases.
Disadvantages of support vector machine : It does not execute very well when the data set has more sound i.e. target classes are overlapping. In cases where the number of properties for each data point outstrips the number of training data specimens, the support vector machine will underperform.
What is SVM? SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs.
Actually, it's usually called incremental learning. The question has come up before and is pretty well answered here : A few implementation details for a Support-Vector Machine (SVM).
In brief, it's possible but not easy, you would have to change the library you are using or implement the training algorithm yourself.
I found two possible solutions, SVMHeavy and LaSVM, that supports incremental training. But I haven't used either and don't know anything about them.
165
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
