Probabilistic neural networks

By Donald F. Specht. 1990

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Citations in the corpus

FearNet: Brain-Inspired Model for Incremental Learning (2017, background): “FearNet’s HC model is a variant of a probabilistic neural network (Specht, 1990).”
Online Continual Learning Via Candidates Voting (2021, methodology): “Prior incorporation: We apply PNN to generate prior probability distribution of which learned task index the test data belongs to.”
A position-specific distance-dependent statistical potential for protein structure and functional study (2012, methodology): “Instead, we apply a probabilistic neural network (PNN) (Specht, 1990) to estimating Pðdja i ; a j ; S i ; S j ; r g Þ when both a i and a j are C a atoms.”
A comprehensive evaluation of multicategory classification methods for microbiomic data (2013, background): “Probabilistic Neural Networks (PNN) belong to the family of Radial Basis Function (RBF) neural networks [22], and are composed of an input layer, a hidden layer consisting of a pattern layer and a competitive layer, and an output layer (see [23,24]).”
Probabilistic neural-network structure determination for pattern classification (2000, unknown): “The PNN was first proposed in [13].”
A review of neural networks in plant disease detection using hyperspectral data (2018, methodology): “Specht [85] introduced the PNN based on the statistical approach called Bayesian classiﬁers.”
Multiclass Support Vector Machines for EEG-Signals Classification (2007, methodology): “On receiving a pattern x from the input layer, the neuron x ij of the pattern layer computes its output where d denotes the dimension of the pattern vector x, σ is the smoothing parameter, and x ij is the neuron vector [14], [15].”
Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review (2007, unknown): “The multi-layer perceptron (MLP) [95], radial basis function network (RBFN) [81], probabilistic neural network (PNN) [104], cascade correlation neural network (Cascor) [28], learning vector quantization (LVQ) [35,56], selforganizing feature map (SOM) [56] are some of”
Extracting decision trees from trained neural networks (1999, methodology): “The "rst is to use the GA-based prototype algorithm to generate a large number of prototypes and then use a prototype selection mechanism to reduce the number of prototypes.”
A comparative study on diabetes disease diagnosis using neural networks (2009, methodology): “The PNN structure is very useful for the classification problems and disease diagnosis systems (Gulbag, 2006; Gulbag, Temurtas, & Yusubov, 2007; Specht, 1990; Temurtas, 2009).”
Machine Learning and Radiology (2012, background): “other important neural networks include radial basis function (RBF) (Moody and Darken, 1989), probabilistic neural (Specht, 1990) and cascading neural networks (Fahlman and Lebiere, 1991).”
Generalized Discriminant Analysis Using a Kernel Approach (2000, methodology): “In PNN the separation is found by trial-and-error measurement on the learning set.”
Damage Detection in Thin Plates and Aerospace Structures with the Electro-Mechanical Impedance Method (2005, methodology): “In the original Specht’s formulation [21], this kernel was expressed as:”