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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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Journal ArticleDOI

ECG arrhythmia classification using a probabilistic neural network with a feature reduction method

TL;DR: The experimental results have successfully validated that the integration of the PNN classifier with the proposed feature reduction method can achieve satisfactory classification accuracy.
Journal ArticleDOI

Using Radial Basis Function Networks for Function Approximation and Classification

TL;DR: Many aspects associated with the RBF network, such as network structure, universal approimation capability, radial basis functions,RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described.
Journal ArticleDOI

FEMa: a finite element machine for fast learning

TL;DR: FEMa—a finite element machine classifier—for supervised learning problems, where each training sample is the center of a basis function, and the whole training set is modeled as a probabilistic manifold for classification purposes, is presented.
Journal ArticleDOI

Chest diseases diagnosis using artificial neural networks

TL;DR: A comparative chest diseases diagnosis was realized by using multilayer, probabilistic, learning vector quantization, and generalized regression neural networks to diagnose chronic obstructive pulmonary, pneumonia, asthma, tuberculosis, lung cancer diseases.
Journal ArticleDOI

A comparison study of chemical sensor array pattern recognition algorithms

TL;DR: The neural network based algorithms (LVQ, PNN, and BP-ANN) have the highest classification accuracies and are recommended for applications where a confidence measure and fast training are critical, while speed and memory requirements are not.
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.