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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4
A Probabilistic RBF Network for Classification
Como, Italy
July 24-July 27
ISBN: 0-7695-0619-4
| ASCII Text | x | ||
| M. Titsias, A. Likas, "A Probabilistic RBF Network for Classification," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 4, pp. 4238, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4, 2000. | |||
| BibTex | x | ||
| @article{ 10.1109/IJCNN.2000.860779, author = {M. Titsias and A. Likas}, title = {A Probabilistic RBF Network for Classification}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {4}, year = {2000}, issn = {1098-7576}, pages = {4238}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.860779}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on TI - A Probabilistic RBF Network for Classification SN - 1098-7576 SP EP A1 - M. Titsias, A1 - A. Likas, PY - 2000 VL - 4 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER - | |||
We present a probabilistic neural network model, which is suitable for classification problems. This model constitutes an adaptation of the classical RBF network where the outputs represent the class conditional distributions. Since the network outputs correspond to probability densities functions, training process is treated as maximum likelihood problem and an Expectation-Maximization (EM) algorithm is proposed for adjusting the network parameters. Experimental results show that proposed architecture exhibits superior classification performance compared to the classical RBF network.
Citation:
M. Titsias, A. Likas, "A Probabilistic RBF Network for Classification," ijcnn, vol. 4, pp.4238, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4, 2000
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