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Quais os artigos mais influentes na área de Aprendizagem de Máquinas?

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perguntada Nov 21, 2015 em Aprendizagem de Máquinas por danielcajueiro (5,251 pontos)  
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respondida Nov 21, 2015 por danielcajueiro (5,251 pontos)  

Uma lista bem incompleta:

Análise dos componentes principais

Pearson, K. On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine v. 2, p. 559–572, 1901.

Regressão binária

Walker, S. H.; Duncan, D. B. "Estimation of the probability of an event as a function of several independent variables. Biometrika v. 54, p. 167–178, 1967.

Cox, D. R. The regression analysis of binary sequences (with discussion. J Roy Stat Soc B, v. 20, p. 215–242, 1958.

Programação Dinâmica e Aprendizagem por Reforço

Barto, A. G., Sutton, R. S., and Anderson, C. W. Neuronlike elements that can solve difficult learning control problems. In IEEE Transactions on Systems, Man, and Cybernetics, v. 13, 835-846, 1983.

Bellman, R. E. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ.

Sutton, R. S. Learning to predict by the methods of temporal differences. Machine Learning, v. 3, p. 9-44, 1988.

Tesauro, G. TD-Gammon, a self-teaching backgammon program, achieves master-level play. Neural Computation v. 6, p. 215-219, 1994.

J. N. Tsitsiklis, and B. Van Roy. Average Cost Temporal-Difference Learning, Automatica, v. 35, p. 1799-1808, 1999.

J. N. Tsitsiklis and B. Van Roy. An Analysis of Temporal-Difference Learning with Function Approximation, IEEE Transactions on Automatic Control, v. 42, p. 674-690, 1997.

Redes Neurais e Backpropagation

Rumelhart, D. E., Hinton, G. E., and Williams, R. J. Learning internal representations by error propagation. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, MIT Press, Cambridge, MA. pp 318-362, 1986.

Rumelhart, D. E., Hinton, G. E., and Williams, R. J. Learning representations by back-propagating errors. Nature, 323, 533--536

Support Vector Machine

Cortes, C.; Vapnik, V. Support-vector networks". Machine Learning v. 20, p. 273, 1995.

Deep Learning

Hinton, G. E. Deterministic Boltzmann learning performs steepest descent in weight-space. Neural computation v. 1, p. 143-150, 1989:

Hinton, G. E. and Salakhutdinov, R. R. (2006) Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.

LeCun, Y., Bengio, Y. and Hinton, G. E. Deep Learning. Nature, v. 521, pp 436-444, 2015.

Boltzman Machines

Hinton, G. E. A practical guide to training restricted Boltzmann machines. Momentum v. 9, p. 926, 2010.

Autoenconders

T. Vincent, H. Larochelle Y. Bengio and P.A. Manzagol, Extracting and Composing Robust Features with Denoising Autoencoders, Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML‘08), pages 1096 - 1103, ACM, 2008.

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