Classification by Density Intersection
Neural Processing Letters
Probability density estimation using entropy maximization
Neural Computation
Probability Density Estimation Using Adaptive Activation Function Neurons
Neural Processing Letters
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Expert Systems with Applications: An International Journal
Fault detection in catalytic cracking converter by means of probability density approximation
Engineering Applications of Artificial Intelligence
Blind source separation based on self-organizing neural network
Engineering Applications of Artificial Intelligence
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An estimate of the probability density function of a random vector is obtained by maximizing the output entropy of a feedforward network of sigmoidal units with respect to the input weights. Classification problems can be solved by selecting the class associated with the maximal estimated density. Newton's optimization method, applied to the estimated density, yields a recursive estimator for a random variable or a random sequence. A constrained connectivity structure yields a linear estimator, which is particularly suitable for “real time” prediction. A Gaussian nonlinearity yields a closed-form solution for the network's parameters, which may also be used for initializing the optimization algorithm when other nonlinearities are employed. A triangular connectivity between the neurons and the input, which is naturally suggested by the statistical setting, reduces the number of parameters. Applications to classification and forecasting problems are demonstrated