2017-08-15 09:51:42 -0600 | asked a question | SVM: How to ignore a feature when using a non-linear kernel using OpenCV 3.1, I'm using the SVM learning algorithm with a non-linear kernel. I implemented a function:
which takes certain information from My training data has all the meta information, but in my use case the images I want to predict on might not have. I thought of two solutions:
I would prefer solution 2, but I've run into a problem zeroing out features in the kerneled space (when using a non-linear kernel). I woud appreciate a way to implement solution 2 or something close. Also, if someone has other recomendations for dealing with missing data I'm open to suggestions. |