  The k-fold cross-validation is commonly used to evaluate the effectiveness of
SVMs with the selected hyper-parameters. It is known that the SVM k-fold
cross-validation is expensive, since it requires training k SVMs. However,
little work has explored reusing the h-th SVM for training the (h+1)-th SVM for
improving the efficiency of k-fold cross-validation. In this paper, we propose
three algorithms that reuse the h-th SVM for improving the efficiency of
training the (h+1)-th SVM. Our key idea is to efficiently identify the support
vectors and to accurately estimate their associated weights (also called alpha
values) of the next SVM by using the previous SVM. Our experimental results
show that our algorithms are several times faster than the k-fold
cross-validation which does not make use of the previously trained SVM.
Moreover, our algorithms produce the same results (hence same accuracy) as the
k-fold cross-validation which does not make use of the previously trained SVM.
