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Lecture Notes in Computer Science
This paper proposes a Mutual Information Independence Model (MIIM) to segment and label sequential data. MIIM overcomes the strong context independent assumption in traditional generative HMMs by assuming a novel pairwise mutual information independence. As a result, MIIM separately models the long state dependence in its state transition model in a generative way and the observation dependence in its output model in a discriminative way. In addition, a variable-length pairwise mutualdoi:10.1007/978-3-540-30586-6_15 fatcat:okhxdsjhtnfdpnkwm75gfb43le