Estimation of Conditional Densities: A Comparison of Neural Network Approaches
In recent years, neural networks have been successfully used to attack a wide variety of difficult nonlinear regression and classification tasks and their effectiveness, particularly when the dimension of the problem measured in the number of variables involved, has been widely documented (Finnoff 1993).
Unable to display preview. Download preview PDF.
- MacKay D. J. C. (1991), Bayesean Modelling and Neural Networks, PhDThesis at California Institute of Technology, PasadenaGoogle Scholar
- Nowlan S. J. (1991), Soft Competitive Adaptation: Neural Network Learning Algorithms based on Fitting Statistical Mixtures, PhD-Thesis at School of Computer Science, Carnegie Mellon University, PittsburghGoogle Scholar
- Ormoneit D. (1993),Estimation of Probability Densities using Neural Networks, Master-Thesis: Dept. of Computer Science, TU MunichGoogle Scholar
- Parzen E. (1962), On Estimation of a Probability Density Function and Mode, Annals of Mathematical Statistics 33Google Scholar
- Redner R. A. and Walker H. F. (1984), Mixture Densities, Maximum Likelihood and the EM Algorithm, SIAM Review, 26Google Scholar
- Specht D. F. (1990), Probabilistic Neural Networks, Neural Networks 3Google Scholar
- Tresp V., Hollatz J. and Ahmad S.(1993) Network Structuring and Training Using Rule-Based Knowledge Advances in NIPS 5Google Scholar
- White H. (1992), Parametrical Statistical Estimation with Artificial Neural Networks, Techreport University of California, San DiegoGoogle Scholar