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- W2167924420 abstract "This paper proposes a new type of neural network called the Dynamic Threshold Neural Network (DTNN) which is theoretically and experimentally superior to a conventional sigmoidal multilayer neural network in classification capability. Given a training set containing 4k + 1 patterns in Rn, to successfully learn this training set, the upper bound on the number of free parameters for a DTNN is (k + 1)(n + 2) + 2(k + 1), while the upper bound for a sigmoidal network is 2k(n + 1) + (2k + 1). We also derive a learning algorithm for the DTNN in a similar way to the derivation of the backprop learning algorithm. In simulations on learning the Two-Spirals problems, our DTNN with 30 neurons in one hidden layer takes only 3200 epochs on average to successfully learn the whole training set, while the single-hidden-layer feedforward sigmoidal neural networks have never been reported to successfully learn the given training set even though more hidden neurons are used." @default.
- W2167924420 created "2016-06-24" @default.
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- W2167924420 date "1994-06-01" @default.
- W2167924420 modified "2023-10-18" @default.
- W2167924420 title "ON THE CLASSIFICATION CAPABILITY OF A DYNAMIC THRESHOLD NEURAL NETWORK" @default.
- W2167924420 doi "https://doi.org/10.1142/s0129065794000128" @default.
- W2167924420 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/7812498" @default.
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