My name is Will Jagy. I advised a youngster yesterday to ask his question here, and he did, Understanding similarity sensitive hashing algorithm in AdaBoost so I suppose I am following up to see if he is in the correct place.
More troubling was this case, https://mathoverflow.net/questions/82499/what-is-the-step-by-step-procedure-for-classifying-a-new-data-point-with-an-rbf-k where some angry words were exchanged. This still bothers me; I do think that EMS made an error in progressing from stackoverflow.com to MO rather than here. Eventually Suvrit, who did not want to be involved, commented "recommend that you have a look at Section 7.4 of the book "Learning with Kernels" by Schölkopf and Smola" which was an answer, plus it turned out that EMS had that book in his office.
To the extent that I have a specific question in the latter case, it is: is the EMS question acceptable here, and would it have received an answer?
There was little to no Latex, I should be able to post the entire title and question here:
What is the step-by-step procedure for classifying a new data point with an RBF kernel SVM?
I've read a ton of tutorials on non-linear SVMs and understand the 'kernel trick' reasonably well. But one thing that is not explained is exactly what you do with some new data once you want to classify it. What do you compute the kernel function on? Suppose I have some data and that I extract a feature vector v. I have a list of coefficients for the higher-dimensional separating hyperplane (this is what the RBF kernel training determines). Now I need to take an inner product between that list of coefficients and something computed from the feature vector v. It's very unclear to me what that thing-computed-from-v is supposed to be.
Added: This non-programming question is partially motivated by wanting to reverse-engineer a function from scikits.learn, so I also asked on Stack Overflow. In tinkering with the scikits.learn code, I have gotten closer to an answer to my math question, so please also check that question too. It is linked here: < https://stackoverflow.com/questions/8360253/how-to-extract-info-from-scikits-learn-classifier-to-then-use-in-c-code >
oc.optimization-control fa.functional-analysis st.statistics