Adaptive probabilistic neural networks for pattern classification in time-varying environment.

Leszek Rutkowski

doi:10.1109/tnn.2004.828757

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Adaptive probabilistic neural networks for pattern classification in time-varying environment.

Leszek Rutkowski

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Abstract

In this paper, we propose a new class of probabilistic neural networks (PNNs) working in nonstationary environment. The novelty is summarized as follows: 1) We formulate the problem of pattern classification in nonstationary environment as the prediction problem and design a probabilistic neural network to classify patterns having time-varying probability distributions. We note that the problem of pattern classification in the nonstationary case is closely connected with the problem of prediction because on the basis of a learning sequence of the length n, a pattern in the moment n + k, k > or = 1 should be classified. 2) We present, for the first time in literature, definitions of optimality of PNNs in time-varying environment. Moreover, we prove that our PNNs asymptotically approach the Bayes-optimal (time-varying) decision surface. 3) We investigate the speed of convergence of constructed PNNs. 4) We design in detail PNNs based on Parzen kernels and multivariate Hermite series.

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Algorithms Artificial Intelligence Computer Simulation Decision Support Techniques Feedback Information Storage and Retrieval Information Theory Models, Statistical Neural Networks, Computer Pattern Recognition, Automated Probability Learning Reinforcement, Psychology Stochastic Processes Time Factors Comparative Study Evaluation Study Journal Article Validation Study

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Leszek Rutkowski. 2004. Adaptive probabilistic neural networks for pattern classification in time-varying environment.. https://doi.org/10.1109/tnn.2004.828757

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