Classification using SVM
I have problem with classification using SVM. Let's say that I have 10 classes, digts from 0 to 9 (or some kind of symbols). I can train SVM to recognize theese classes, but sometimes I get image which is not digt, but SVM still tries to categorize this image. Is there a way to set threshold for SVM on the output maybe (as I can set it for Neural Networks) to reject bad images? May I ask for code sample (in C++ or Python with opencv)? Thanks in advance.
As suggested in several topics before you could use the distance to the decision plane as a score for how good your SVM is classifying. This means the larger the value, the more certain you are that the element is assigned to that specific class. Use that value to threshold and only keep the most certain predictions?