INFERENCE AND LEARNING IN STOCHASTIC AUTOMATA
K.-H. Zimmermann · International Journal of Pure and Apllied Mathematics · 2017
Machine learning provides algorithms that can learn from data and make inferences or predictions on data.Stochastic automata are a class of input/output devices which can model components in machine learning scenarios.In this paper, we provide an inference algorithm for stochastic automata which is related to the Viterbi algorithm.Moreover, we specify a learning algorithm using the expectation-maximization technique and describe a more efficient implementation which is related to the Baum-Welch algorithm.