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Lecture Notes in Computer Science
Hidden Markov Models (HMMs) are an useful and widely utilized approach to the modeling of data sequences. One of the problems related to this technique is finding the optimal structure of the model, namely, its number of states. Although a lot of work has been carried out in the context of the model selection, few work address this specific problem, and heuristics rules are often used to define the model depending on the tackled application. In this paper, instead, we use the notion ofdoi:10.1007/3-540-44745-8_6 fatcat:vll3sjpiobdg7cs2dpv32yqdpu