Semi-supervised learning has received much attention recently.Co-training is a kind of semi-supervised learning method which uses unlabeled data to boost the performance of standard supervised learning algorithms.A novel co-training style algorithm

RASCO(for RAndom Subspace CO-training)

is proposed which uses stochastic discrimination theory to extend co-training to multi-view situation.The accuracy and generalizability of RASCO are analyzed.The influences of the parameters of RASCO are discussed.Experiments on UCI data set demonstrate that RASCO is more effective than other co-training style algorithms.