Cited By
View all- Chowdhary KChowdhary K(2020)Statistical Learning TheoryFundamentals of Artificial Intelligence10.1007/978-81-322-3972-7_14(415-443)Online publication date: 5-Apr-2020
Technical perspective
Machine learning for complex predictions
Abstract
No abstract available.
References
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Joachims, T. Transductive inference for text classification using support vector machines. In Proceedings of the 16th International Conference on Machine Learning (Bled, Slovenia, 1999), 200--209.
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Minsky, M.L. and Papert, S.A. Perceptrons. MIT Press, Cambridge, MA, 1969.
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Novikoff, A.B. On convergence proofs on perceptrons. Symposium on the Mathematical Theory of Automata. Polytechnic Institute of Brooklyn (1962), 615--622.
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Rosenblatt, F. The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65, 6 (1958), 386--408.
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Rumelhart, D.E., Hinton, G.E., and Williams, R.J. Learning representations by backpropagating errors. Nature 323 (1986), 533--536.
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Taskar, B., Guestrin, C., and Koller, D. Maxmargin markov networks. Advances in Neural Information Processing Systems. S. Thrun, L. Saul, and B. Schölkopf, Eds. MIT Press, Cambridge, MA, 2004, 25--32.
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Vapnik, V. Statistical Learning Theory. John Wiley, 1998.
Index Terms
- Technical perspective
Machine learning for complex predictions
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 November 2009
Published in CACM Volume 52, Issue 11
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Cited By
View all- Chowdhary KChowdhary K(2020)Statistical Learning TheoryFundamentals of Artificial Intelligence10.1007/978-81-322-3972-7_14(415-443)Online publication date: 5-Apr-2020
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