Abstract: This paper proposed a novel smooth regularization based semi-supervised support vector machine approach,and applied it to set up credit evaluation model of small-and-medium enterprises．It computes a manifold-related smooth regularization term on both few labeled samples and plenty of unlabeled samples,which is combined into the learning process of maximal margin classifiers．Then,it adopts a progressive method to acquire semi-labeled samples iteratively so that the generalization performance of support vector machine can be improved gradually．Experiments on reality dataset show that the testing accuracy of proposed approach outperforms several popular ones,and is very suitable for evaluating credit grades of small-and-medium enterprises．

Key words: Support vector machines,Credit evaluation,Semi-supervised learning,Smooth regularization