SVM Classification - minimum number of input sets for each class
- by Amol Joshi
Im trying to build an app to detect images which are advertisements from the webpages. Once I detect those Ill not be allowing those to be displayed on the client side.
From the help that I got here in stackoverflow, I thought SVM is the best approach to my aim.
So, I have coded SVM and an SMO myself. The dataset which I have got from UCI data repository has 3280 instances ( Link to Dataset- http://archive.ics.uci.edu/ml/datasets/Internet+Advertisements )where around 400 of them are from class representing Advertisement images and rest of them representing non-advertisement images.
Right now Im taking the first 2800 input sets and training the SVM. But after looking at the accuracy rate I realised that most of those 2800 input sets are from non-advertisement image class. So Im getting very good accuracy for that class.
So what can I do here? About how many input set shall I give to SVM to train and how many of them for each class?
Thanks. Cheers. ( Basically made a new question because the context was different from my previous question. http://stackoverflow.com/questions/1991113/optimization-of-neural-network-input-data )
