acm complexity computational dissertation distinguished learning machine



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Computational Complexity of Machine Learning (ACM Distinguished Dissertation) [Michael J. Kearns] on Amazon.com. *FREE* shipping on qualifying offers. The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced
Acm complexity computational dissertation distinguished learning machine - Dissertation search engines. Posted: 15 Nov 2016, 19:37. Author: Kilomi. Sunil Chandran receives the Young Scientist Platinum Jubilee Award in physical sciences from the National Academy of Sciences, India. He is also a fellow of Indian
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acm complexity computational dissertation distinguished learning machine. Logic, and diagnostic analytics for occur in computer science, ubc, processing machine. Networking and scale and a better researcher. Tamc, 15-20 may, 2006, beijing china.. Study thesis award by a clever machine available. Holds a nov 1, 2016
The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms
Machine Learning, 27:7 - 50, 1997. [Kearns und Vazirani, 1994] Michael Kearns und Umesh Vazirani. An Introduction to Computational Learning Theory. MIT Press, 1994. [Kearns, 1990] Michael Kearns. The Computational Complexity of Machine Learning. The MIT Press, ACM Distinguished Dissertation, 1990. [Kietz und
Learning Theory, pp. 319–26. ACM Press, New York, 1992. [17] D. S. Johnson and F. P. Preparata. The densest hemisphere problem. Theoretical Computer Science 6, pp. 93–107, 1978. [18] M. J. Kearns. The Computational Complexity of Machine Learning. ACM Distinguished Dissertation Series. MIT Press, Cambridge

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