Mixture of HMM Experts with applications to landmine detection

This paper introduces a novel mixture of experts model, the Mixture of Hidden Markov Model Experts (MHMME). This model is designed to perform context-based classification of samples that are variable length sequences. The contexts are determined by the gates and the classifiers are determined by the experts. The gates and the experts are learned simultaneously using a single probabilistic model. Experimental results on landmine dataset show that MHMME significantly outperforms the HMM-based and ME-based models.