Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Estimation of Classifier Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
On the implementation of RBF technique in neural networks
ANNA '91 Proceedings of the conference on Analysis of neural network applications
Fuzzy expert systems architecture for image classification using mathematical morphology operators
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
Neural Learning from Unbalanced Data Using Noise Modeling
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Neural Learning from Unbalanced Data
Applied Intelligence
Independent residual analysis for temporally correlated signals
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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This correspondence presents a method for evaluation of artificial neural network (ANN) classifiers. In order to find the performance of the network over all possible input ranges, a probabilistic input model is defined. The expected error of the output over this input range is taken as a measure of generalization ability. Two essential elements for carrying out the proposed evaluation technique are estimation of the input probability density and numerical integration. A nonparametric method, which depends on the nearest M neighbors, is used to locally estimate the distribution around each training pattern. An orthogonalization procedure is utilized to determine the covariance matrices of local densities. A Monte Carlo method is used to perform the numerical integration. The proposed evaluation technique has been used to investigate the generalization ability of back propagation (BP), radial basis function (RBF) and probabilistic neural network (PNN) classifiers for three test problems.