I'm trying to implement the SVM algorithm manually, and I've succeeded in doing so with the case of a linear kernel (no kernel function).
Now I'd like to add a gaussian kernel to the algorithm, but I'm facing a size mismatch problem. The way I understand it, is that by applying your original examples to the kernel you create a new feature matrix. If I have m training examples then the new kernel matrix would become an (m×m) matrix regardless of how many features I had, and the weights vector would be of length (m+1).
Then when I apply a test set to algorithm with k examples and each example having n features, I get a dimension mismatch error: n != m+1
What am I missing?