The power of amnesia: Learning probabilistic automata with variable memory length

@article{Ron1996ThePO,
  title={The power of amnesia: Learning probabilistic automata with variable memory length},
  author={Dana Ron and Yoram Singer and Naftali Tishby},
  journal={Machine Learning},
  year={1996},
  volume={25},
  pages={117-149}
}
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 Suffix Automata (PSA). Though hardness results are known for learning distributions generated by general probabilistic automata, we prove that the algorithm we present can efficiently learn distributions generated by PSAs. In particular, we show that for any target PSA, the KL-divergence… 
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