W03-0434 minimization . The generalized Winnow method in ( Zhang et al. , 2002 ) implements
W03-1026 risk function . The generalized Winnow method used in ( Zhang et al. , 2002
W03-0408 this paper we use the generalized Winnow method ( Zhang et al. , 2002 ) for all
P01-1069 the performance of the original Winnow method . The improvement is more or
P01-1069 Winnow method . Clearly regularized Winnow method has indeed enhanced the performance
P01-1069 same as that of the regularized Winnow method . Clearly regularized Winnow
P01-1069 results with those of the original Winnow method . We only report results with
P01-1069 algorithm and the regularized Winnow method . Consider the binary classification
W03-0408 mentioned earlier , the generalized Winnow method approximately minimizes the quantity
E06-1030 newspaper text . Compared to the Winnow method , the 10 billion word Web Corpus
E06-1030 exceeding the accuracy of the Unpruned Winnow method ( the only other true cross-corpus
P01-1069 Winnow method over the original Winnow method can be much more significant
P01-1069 the improvement of regularized Winnow method over the original Winnow method
P01-1069 non-convergence problem of the original Winnow method when the data are not linearly
W03-0425 fier , based on a regularized winnow method ( Zhang et al. , 2002 ) ( henceforth
W03-0408 computed from the generalized Winnow method , which is based on the following
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