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Unformatted text preview: that reduce the multi class task to several binary problems have to be applied, see the
Multi class SVM section.
3.How to select the kernel function parameters  for Gaussian kernels the width parameter , and the value of in the  insensitive loss function have not entirely solved yet.
some resouces:
1. introduction of SVM[36] (http://www.fml.tuebingen.mpg.de/raetsch/lectures/ismb09tutorial/images/WhatIsASVM.pdf)
2. SVM in computational biology[37] (http://noble.gs.washington.edu/papers/noble_support.pdf)
wikicour senote.com/w/index.php?title= Stat841&pr intable= yes 70/74 10/09/2013 Stat841  Wiki Cour se Notes Finishing up SVM  November 25, 2009
Does SVM find a global minimum?
When we discussed KKT conditions, we listed the necessary conditions for
minimum (unlike, say, neural networks that find a local minimum).
Recall that our conditions, for the non separable case, are
Our lagrangian equation is to be a local minimum. However, it would be ideal if we could show that SVM finds a global and . These are both convex. . Since this is quadratic, it might be convex, but it also may not be; it depends on the matrix . If is a positive semi definite matrix, then the lagrangian equation is convex. Recall that is the product of , where . Similar to the notion that squaring any number will give us a positive number in the end, a matrix that is the product of a matrix transposed times itself will result in a positive semi definite matrix.
So, we know that is positive semi definite. The lagrangian equation is convex, and therefore, the SVM algorithm finds a global minimum. Naive Bayes, Decision Trees, K Nearest Neighbours, Boosting, and Bagging  November 25, 2009
Now that we've covered a number of more advanced classification algorithms, we can look at some of the simpler classification algorithms that are usually discussed at the
beginning of a discussion on classification. Naive Bayes Clas s ifiers (http://en.wikipedia.org/wiki/Naive_Bayes _clas s ifier)
Recall that one of the major drawbacks of the...
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 Winter '13

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