论文信息 - Neural network for estimating conditional distributions

Neural network for estimating conditional distributions

Neural networks for estimating conditional distributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency is proved from a mild set of assumptions. A number of applications within statistics, decision theory, and signal processing are suggested, and a numerical example illustrating the capabilities of the elaborated network is given.

Henrik Schiøler | Piotr Kulczycki | P. Kulczycki | H. Schiøler

[1] Henrik Schiøler,et al. Parameter Identification by Bayes Decision and Neural Networks , 1994 .

[2] Halbert White,et al. Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.

[3] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .

[4] Piotr Kulczycki,et al. Time-Optimal Stochastic Positional Control , 1993 .

[5] Ashok N. Srivastava,et al. Predicting conditional probability distributions: a connectionist approach , 1995, Int. J. Neural Syst..

[6] James S. Harris,et al. Probability theory and mathematical statistics , 1998 .

[7] W. H. Williams,et al. Probability Theory and Mathematical Statistics , 1964 .

[8] D. F. Specht,et al. Probabilistic neural networks for classification, mapping, or associative memory , 1988, IEEE 1988 International Conference on Neural Networks.

[9] Donald F. Specht,et al. A general regression neural network , 1991, IEEE Trans. Neural Networks.

[10] Uwe Hartmann,et al. Mapping neural network derived from the parzen window estimator , 1992, Neural Networks.

[11] John Moody,et al. Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[12] Bruce W. Suter,et al. The multilayer perceptron as an approximation to a Bayes optimal discriminant function , 1990, IEEE Trans. Neural Networks.