Learning Probabilistic Automata with Variable Memory Length

  title={Learning Probabilistic Automata with Variable Memory Length},
  author={Dana Ron and Yoram Singer and Naftali Tishby},
We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Finite Suffix Automata. The learning algorithm is motivated by real applications in man-machine interaction such as hand-writing and speech recognition. Conventionally used fixed memory Markov and hidden Markov models have either severe practical or theoretical drawbacks. Though general… CONTINUE READING
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