Adding "ambiguous" class to SVM
I've trained an SVM to classify images of tags my program can extract, however I used data where I could tell which tag type was which myself visually. Unfortunately, for maybe half the tag images I can't tell which type it belongs to (and thus didn't include it in my training set), and as a result the SVM classifies perfectly on my training data (when I split a training set from it), but with the real data it's wrong about half the time. Would the best option here be to include an "ambiguous" class of data to my SVM with several hundred/thousand images of the tags where I can't tell what type it is? I'd rather have more instances of "unknown" classifications and be right more often when I do try to make a prediction.
what, if you train a seperate (binary)SVM for each of your 'known' classes, one-vs-all style ?
So if I had 3 classes, I should train 3 SVMs, and then look at the ones that classifies the tag in a 'known' category than see how confident it is?
yes, exactly.
I'll give it a go :) thanks for the advice