Fuzzy support vector machines with the uncertainty of parameter C

In typical pattern recognition applications, there are usually only some vague and general knowledge about the situation. An optimal classifier, then, will be definitely hard to develop if the decision function lacks sufficient knowledge. The aim of our experiments was to extract some features by some appropriate transformation of the training set. In the paper, we assumed that the training samples were drawn from a Gaussian distribution. Also, assumed that if the data sets are in an imprecise situation, such as classes overlap, it can be represented by fuzzy sets. Results showed a powerful learning capacity: the fuzzy support vector machines with the uncertainty of parameter C rule (FSVMs-UPC) was proposed. Here it indicated that each data point had individual parameter coefficient been valuable. The experimental results show that the proposed method is a better way to postpone or avoid overfitting, and it also gives us a measure of the quality of the ultimately chosen model.