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In biological systems such precise switches are not present at birth, but created by honing down of ensemble action onto one or a small number of units by training of a network.
Cross-validation is a highly recommended criterion for stopping the training of a network.
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Here we trained on a network of regulatory interactions extracted from TRANSFAC; others have used regulation data from RegulonDB [ 43] or KEGG (Kyoto Encyclopedia of Genes and Genomes) [ 72].
In the second phase, the parameters of the model are tuned via the training of a neural network through backpropagation.
A sequential orthogonal approach to the building and training of a neural network with a single radial basis function (RBF) neuron is presented in this paper.
This study concerns the training of a neural network in multiple stages considering minimization of errors from multiple data/pattern resources.
This chapter provide the basic vocabulary used to describe neural networks, especially the multi-layer Perceptron architecture (MLP) using the backpropagation learning algorithm and provides a description of the variables the user can control during training of a neural network.
The training of a cortical network has three phases: 1. Random initialization: Initially, all the synaptic weighs are initialized to random values that are very close to 0, so that no preference for any particular pattern is shown.
Another trend is related to an indirect use of PSO technique for estimation, more precisely for the training of a neural network, which models the bioprocess [ 42].
Here, we presented an algorithm for fully automatic detection of neuronal soma in CLSM fluorescence images, in which human supervision is needed only for the initial training of a neural network.
The training of an ANFIS network which involves grade estimation, conducts the input training vectors to target vectors with a minimum total sum of squared error.
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