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- W2384374611 abstract "This paper presents a new type of neural network for pattern classification namely Dynamic Supervised Forward Propagation Network(DSFPN). Although the network is based on the improved forward-only version of the Counterpropagation Network(CPN), its hidden layer is trained in a supervised manner by using a modified second version of Learning Vector Quantization(LVQ2) algorithm, and an incremental training strategy is adopted. New hidden neurons are created based on a measure of suitability during the training with dynamic growth of the hidden layer. Experimental results of Cone-Torus planar point classification and speaker-independent isolated digital word speech recognition show that DSFPN requires two order of magnitude less training time than the Multilayer Perceptron(MLP), and has higher training speed than the improved CPN, while the best testing accuracies achieve 92.25% and 98.7%, better than the two networks." @default.
- W2384374611 created "2016-06-24" @default.
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- W2384374611 date "2008-01-01" @default.
- W2384374611 modified "2023-09-26" @default.
- W2384374611 title "Dynamic Supervised Forward Propagation Network for Pattern Classification" @default.
- W2384374611 hasPublicationYear "2008" @default.
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