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Journal ArticleDOI

Probabilistic neural networks

Donald F. Specht
- 01 Jan 1990 - 
- Vol. 3, Iss: 1, pp 109-118
TLDR
A probabilistic neural network that can compute nonlinear decision boundaries which approach the Bayes optimal is formed, and a fourlayer neural network of the type proposed can map any input pattern to any number of classifications.
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This article is published in Neural Networks.The article was published on 1990-01-01. It has received 3772 citations till now. The article focuses on the topics: Probabilistic neural network & Activation function.

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Citations
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New self-adaptive probabilistic neural networks in bioinformatic and medical tasks

TL;DR: A self–adaptive probabilistic neural network model, which incorporates optimization algorithms to determine its spread parameters is proposed, which is investigated on two protein localization problems, as well as on two medical diagnostic tasks.
Journal ArticleDOI

Application of neural networks and statistical pattern recognition algorithms to earthquake risk evaluation

TL;DR: The experimental results on the application of different pattern recognition algorithms to the evaluation of earthquake risk for real geological structures and the advantages provided by the use of methods for combining multiple classifiers are reported.
Journal ArticleDOI

Classification of clustered microcalcifications using a Shape Cognitron neural network

TL;DR: A new shape recognition-based neural network built with universal feature planes, called Shape Cognitron (S-Cognitron) is introduced to classify clustered microcalcifications to improve sensitivity and specificity of mammogram database records.
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The application of artificial neural networks in the prediction of microemulsion phase boundaries in PEG-8 caprylic/capric glycerides based systems

TL;DR: Results confirmed that the trained GRNN could be effective in predicting the size of the microemulsion area providing valuable tool in formulation of this type of colloidal vehicles.
Journal ArticleDOI

Fetal State Assessment from Cardiotocogram Data Using Artificial Neural Networks

TL;DR: A comparative study of fetal state assessment using machine learning methods from cardiotocogram data using three artificial neural network models, namely the multilayer perceptron neural network, probabilistic neuralnetwork, and generalized regression neural network is presented.
References
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Journal ArticleDOI

Nearest neighbor pattern classification

TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Journal ArticleDOI

On Estimation of a Probability Density Function and Mode

TL;DR: In this paper, the problem of the estimation of a probability density function and of determining the mode of the probability function is discussed. Only estimates which are consistent and asymptotically normal are constructed.
Book

Introduction to the Theory of Statistics

TL;DR: In this article, a tabular summary of parametric families of distributions is presented, along with a parametric point estimation method and a nonparametric interval estimation method for point estimation.
Journal ArticleDOI

Introduction to the Theory of Statistics.

TL;DR: In this article, a tabular summary of parametric families of distributions is presented, along with a parametric point estimation method and a nonparametric interval estimation method for point estimation.