Abstract
An improvement to the Probabilistic Neural Network (PNN) is presented that overcomes two weaknesses of the original model. In the new model, due to the fact that each neuron uses its own Gaussian kernel function, a better generalization ability is achieved by the means of stretching and rotation leading to the Rotated Kernel Probabilistic Neural Network (RKPNN). Furthermore, an algorithm is presented that calculates automatically the kernel parameters of each Gaussian function. The covariance matrices will be subdivided into two other matrices R and S that are calculated separately. This training is slower than that of the original PNN, but in its complexity comparable with other classification methods. A real-world example finally prooves that the proposed model shows good generalization capacity with similar or even slightly better results than other approaches.
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References
Berthold M.R., Diamond J. (1998), Constructive training of probabilistic neural networks. Neurocomputing, 19, 167–183
C.L. Blake and C.J. Merz (1998),UCI Repsoitory of machine learning databases, http://www.ics.uci.edu/~/mlearn/MLRepository.html, University of California, Irvine, Dept. of Information and Computer Sciences
Chen C.H., You G.H. (1992), ISBN Recognition Using a Modified Probabilistic Neural Network (PNN). Proceedings 11th IAPR International Conference on Pattern Recognition, Vol.II, 419–421
Collobert R., Bengio S. (2001), SVMTorch: Support Vector Machines for Large-Scale Regression Problems, Journal of Machine Learning Research, Vol 1, 143–160
Galleske I., Castellanos J. (1997), Probabilistic Neural Networks with Rotated Kernel Functions Proceedings 7th Internation Conference on Artificial Neural Networks ICANN’97, 379–384
Musavi M.T., Chan K.H., Hummels D.M., Kalantri K (1993), On the Generalization Ability of Neural Network Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.16, No.6, 659–663
Parzen E. (1962), On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33, 1065–1076
Specht D.F. (1988), Probabilistic neural networks for classification mapping, or associative memory. Proceeding, IEEE International Conference on Neural Networks, 1, 525–532
Specht D.F. (1990), Probabilistic Neural Networks. Neural Networks, 3, 109–118
Specht D.F. (1991), Generalization accuracy of probabilistic neural networks compared with backpropagation networks. IJCNN-91-Seattle: International Joint Conference on Neural Networks, Vol.1, 887–892
Yang Z.R., Chen S. (1998), Robust maximum likelihood training of heteroscedastic probabilistic neural networks. Neural Networks, 11, 739–747
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© 2003 Springer-Verlag Berlin Heidelberg
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Galleske, I., Castellanos, J. (2003). A Rotated Kernel Probabilistic Neural Network (RKPNN) for Multi-class Classification. In: Mira, J., Álvarez, J.R. (eds) Computational Methods in Neural Modeling. IWANN 2003. Lecture Notes in Computer Science, vol 2686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44868-3_20
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DOI: https://doi.org/10.1007/3-540-44868-3_20
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