Adaptive probabilistic neural networks for pattern classification in time-varying environment.
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.
Department of Computer Engineering, Technical University in Czestochowa. lrutko@kik.pcz.czest.pl
Leszek
L
Rutkowski
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D005246:Feedback; D016247:Information Storage and Retrieval / Q000379:methods*; D007257:Information Theory; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011337:Probability Learning; D012054:Reinforcement, Psychology; D013269:Stochastic Processes; D013997:Time Factors