A New Algorithm for Mixture Splitting Based on DBC

EM (expectation-maximization) algorithm is a classical method for parameter estimation of HMM (Hidden Markov model ). Concerning that EM algorithm is easily affected by initial parameter values, we proposed a mixture splitting algorithm based on decision boundary confusion (DBC) to describe more about boundary distribution. The algorithm mainly includes three aspects: firstly the number of incremented mixtures for every decision boundary could be determined according to decision boundary confusion; secondly the mixtures which are the closest to the decision boundary are chosen to split, thirdly the split mean of mixture is in the direction of decision boundary. Our experiments show that our proposed algorithm is more effective for classification using HMM.