How to use svmtrain() with a custom kernel in Matlab?

0
votes

svmtain() is a function in MATLAB for SVM learning. The help doc is here:

http://www.mathworks.com/help/bioinfo/ref/svmtrain.html

How can I use it with a custom kernel? In the help doc, it says:

@kfun — Function handle to a kernel function. A kernel function must be of the form

 function K = kfun(U, V)

The returned value, K, is a matrix of size M-by-N, where U and V have M and N rows respectively.

It mentions nothing about what U and V are and what M and N mean. I just don't know how to use it in the right format. Can anyone tell me what U and V are and what M and N mean? For example, the training data are 5-dimensional vectors and the kernel function is the sum of the length of the vectors. How can I write the kernel function?

Thank you!

2
matlabmachine-learning
writing your own kernel function is not trivial at all ( crsouza.blogspot.sk/2010/03/… ), you need some functional programming knowledge, ... I'd strongly suggest you to use some of the predefined kernel. You'll get better results unless you are experienced ML researcher with deep theoretical background - xhudik
Thank you. I have already got my own kernel function and want to test it using matlab. This is why I ask the question here. Can you help explain what U and V are and what M and N mean? - D. Chen

2 Answers

1
votes

just a guess:

according to: http://www.tech.dmu.ac.uk/~hseker/Statistics%20in%20Genetics/Statistical%20Learning%20and%20Visualization%20in%20MATLAB.doc , U, V should be just parameters in your functional prescription K, e.g. if your kernel is tanh, then:

function K = kfun(U,V,P1,P2)
K = tanh(U*V');

And P1, P2 are for some additional features of your respective kernel. But as I wrote in the comment, you need to be good mathematician to reach better results than the ones acquired by already defined kernels.

0
votes

Kernel functions are one of the most common techniques used at Machine Learning algorithms. Here is definition of it from Wikipedia:

For machine learning algorithms, the kernel trick is a way of mapping observations from a general set S into an inner product space V (equipped with its natural norm), without ever having to compute the mapping explicitly, in the hope that the observations will gain meaningful linear structure in V.

i.e. this kernel is used at RBF:

K(x,y) = (x*y + c)^d

Here is an detailed explanation of Kernels: http://www.youtube.com/watch?v=bUv9bfMPMb4 by Andrew Ng.

There are some Kernels (i.e. Gaussian Kernel), kernels have same convention thats why it is generalized as K(u,v). You can try different kernels performances or you can search about related works about what you work on and try to use that kind of kernels.