Paper announcement: SVM in nonstandard situations

[Yi Lin]

Dear Connectionists,

A paper on the support vector machines for classification in nonstandard 
situations (with unequal misclassification cost, sampling bias present) 
is now available online:

http://www.stat.wisc.edu/~yilin or http://www.stat.wisc.edu/~wahba

Title and abstract are below:

---------------------------------------------------------------------------

    Support Vector Machines for Classification in Nonstandard Situations

                  Yi Lin, Yoonkyung Lee, and Grace Wahba

The majority of classification algorithms are developed for the standard 
situation in which it is assumed that the examples in the training set come 
from the same distribution as that of the target population, and that the 
cost of misclassification into different classes are the same. However, these 
assumptions are often violated in real world settings. For some classification
 methods, this can often be taken care of simply with a change of threshold; 
for others, additional effort is required. In this paper, we explain why the 
standard support vector machine is not suitable for the nonstandard situation,
 and introduce a simple procedure for adapting the support vector machine 
methodology to the nonstandard situation. Theoretical justification for the 
procedure is provided. Simulation study illustrates that the modified support 
vector machine significantly improves upon the standard support vector machine
 in the nonstandard situation. The computational load of the proposed 
procedure is the same as that of the standard support vector machine. The 
procedure reduces to the standard support vector machine in the standard 
situation.