The Greedy EM algorithm is a recently proposed method that tries to overcome this drawback, in the case of the density estimation problem using mixture models.
In this work we pro- pose a similar approach for incremental training of the PRBF network for classification. The proposed algorithm starts with a single compo-.
Abstract. The Probabilistic RBF (PRBF) network constitutes an a- daptation of the RBF network for classification. Moreover it extends the.
We show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from generalised linear models. This approach is compared ...
Missing: Probabilistic | Show results with:Probabilistic
The Probabilistic RBF (PRBF) network constitutes an adaptation of the RBF network for classification. Moreover it extends the typical mixture model by allowing ...
New structure and training algorithms of the RBF-type neural network are proposed. An extra neuron layer is added to realize the principal component method.
Missing: Probabilistic | Show results with:Probabilistic
We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which ...
Missing: Probabilistic | Show results with:Probabilistic
We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which ...
Efficient training of RBF networks for classification - ResearchGate
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Oct 22, 2024 · We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm.
We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which ...
Missing: Probabilistic | Show results with:Probabilistic