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Learning to Model Sequences Generated by Switching Distributions by Yoav Freund; Dana Ron is a scholarly article available to read on EtoBox.

What is Learning to Model Sequences Generated by Switching Distributions about?

We study efficient algorithms for solving the following problem, which we call the switching distributions learning problem.A sequence S = alaz... an, over a finite alphabet Z is generated in the following way. The sequence is a concatenation of K runs, each of which is a consecutive subsequence. Each run is generated by independent random draws from a distribution 17i over Z, where $% is an element in a set of distributions {p,,... , f?~}. The learning algorithm is given this sequence and its goal is to find approximations of the distributions ~1, . . . . $IV, and give an approximate segmentation of the sequence into its constituting runs. We give an efficient algorithm for solving this problem and show conditions under which the algorithm is guaranteed to work with high probability.

Author

Yoav Freund; Dana Ron

Publisher

ACM Press

Published

1995

Language

EN