Enhanced probabilistic neural network with local decision circles: A robust classifier

By Mehran Ahmadlou, Hojjat Adeli. Integr. Comput. Aided Eng., 2010

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/2caefc9651b919c7320622abb55472db9b385174

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Citations in the corpus

Application of Recurrence Quantification Analysis for the Automated Identification of Epileptic EEG Signals (2011, background): “The second layer is the competitive layer that sums these contributions for each input classes and produces a vector of probabilities as its output.(8,11) The ‘compete’ transfer function at the output of the second layer selects the maximum of these probabilities, and assigns a 1 for that selected c”
Efficient machine learning models for prediction of concrete strengths (2021, background): “Moreover, many more power177 ful neural network models have been recently proposed to handle structured data [37, 38].”
Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. (2015, unknown): “Examplesofclassificationalgorithmsusedforseizuredetection andepilepsydiagnosisare:k-NearestNeighboralgorithm(k-NN) [31],ProbabilisticNeuralNetwork(PNN)[32],Fisher'slinear discriminant(FLD)[33],SupportVectorMachine(SVM)[34], OptimumPathForest(OPF)[35],PrincipalComponentAnalysis (PCA)[36],andEnhancedP”
A comparative study of wavelet families for EEG signal classification (2011, background): “029 more suitable to medical application since it uses Bayesian strategy, a process familiar to medical decision makers [4,24,25,35].”
A New Neural Dynamic Classification Algorithm (2017, unknown): “Index Terms— Classification algorithm, Mixed National Institute of Standards and Technology (MNIST), neural dynamic model of Adeli and Park, probabilistic neural networks (PNNs), supervised learning.”
FEMa: a finite element machine for fast learning (2019, background): “Some years later, Ahmadlou and Adeli [7] proposed the enhanced probabilistic networks (EPNNs), a clever way to penalize outliers when computing the inﬂuence of the Gaussian distribution over the training samples.”