论文信息 - Estimating conditional distributions by neural networks

Estimating conditional distributions by neural networks

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 property is considered from a mild set of assumptions. A number of applications within statistics, decision theory and signal processing are suggested.

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

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

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

[4] Henrik Schiøler,et al. Neural network for estimating conditional distributions , 1997, IEEE Trans. Neural Networks.

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

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

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

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

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

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