DocumentCode :
3317650
Title :
A soft probabilistic neural network for implementation of Bayesian classifiers
Author :
Menhaj, Mohammad B. ; Delgosha, Farshid
Author_Institution :
Dept. of electr. Eng., Amirkabir Univ., Tehran, Iran
Volume :
1
fYear :
2001
fDate :
2001
Firstpage :
454
Abstract :
A classifier with the optimum decision, Bayesian classifier could be implemented with probabilistic neural networks (PNNs). The authors presented a new competitive learning algorithm for training such a network when all classes are completely separated. This paper generalizes our previous work to the case of overlapping categories. In our new perspective, the network is, in fact, made blind with respect to the overlapping training samples, so the new training algorithm is called soft PNN (or SPNN). The usefulness of SPNN has been proved by two 2-D classification problems. The simulation results highlight the merit of the proposed method
Keywords :
Bayes methods; covariance matrices; estimation theory; neural nets; random processes; signal classification; unsupervised learning; 2D classification problems; Bayesian classifiers; competitive learning algorithm; optimum decision; overlapping categories; overlapping training samples; soft probabilistic neural network; Electronic mail; Maximum likelihood estimation; Neural networks; Neurons; Parametric statistics; Polynomials; Robustness; Statistical distributions; Stochastic processes; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-7044-9
Type :
conf
DOI :
10.1109/IJCNN.2001.939062
Filename :
939062
Link To Document :
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