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in Machine Learning by (19k points)

In a particular application I was in need of machine learning (I know the things I studied in my undergraduate course). I used Support Vector Machines and got the problem solved. Its working fine.

Now I need to improve the system. Problems here are

I get additional training examples every week. Right now the system starts training freshly with updated examples (old examples + new examples). I want to make it incremental learning. Using previous knowledge (instead of previous examples) with new examples to get new model (knowledge)

Right my training examples has 3 classes. So, every training example is fitted into one of these 3 classes. I want functionality of "Unknown" class. Anything that doesn't fit these 3 classes must be marked as "unknown". But I can't treat "Unknown" as a new class and provide examples for this too.

Assuming, the "unknown" class is implemented. When class is "unknown" the user of the application inputs the what he thinks the class might be. Now, I need to incorporate the user input into the learning. I've no idea about how to do this too. Would it make any difference if the user inputs a new class (i.e.. a class that is not already in the training set)?

Do I need to choose a new algorithm or Support Vector Machines can do this?

1 Answer

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by (33.1k points)

Multi-Layer Perceptrons can implement incremental learning, because the training instances don't affect the basic network architecture, they'll just adjust the values of the weight matrices. One additional training instance could change the selection of the support vectors. 

LIBSVM in one-against-many approach i.e., as a one-class classifier can be used. SVMs are one-class classifiers. There are some application of an SVM for multi-class means that it has been coded to perform multiple, step-wise one-against-many classifications. 

Hope this answer helps.

If you want to know about Support Vector Machine visit this Support Vector Machine Tutorial.

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