1. N. Abe and M. K. Warmuth On the Computational Complexity of Approximating Distributions by Probabilistic Automata. To appear, Machine Learning.
2. David Haussler. Generalizing the PAC model for neural net and other learning applications. Technical Report UCSC CRL-89–30, University of California at Santa Cruz, September 1989. Extended abstract appeared in Proceedings of IEEE Symposium on Foundations of Computer Science, October, 1989.
3. M. Kearns and R. Schapire. Efficient distribution-free learning of probabilistic concepts. Proceedings of IEEE Symposium on Foundations of Computer Science, October, 1990.
4. A lower bound for discrimination in terms of variation;Kullback;IEEE Trans, on IT,1967
5. An introduction to the application of the theory of probabilistic functions of a markov process to automatic speech recognition;Levinson;The Bell System Technical Journal,1983