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The pattern weights are iteratively bipartitioned into subsets based upon the size of the weights; each successive partition isolates the subset with the largest pattern weights.
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The pattern weights were uncorrelated with each other, r=−0.04.
Associations of functional gene sets with mark levels and basis pattern weights were done using the random-set method [ 65]. (an implementation of the random-set method is included in the source-code distribution of epicode).
The 5-mer motif patterns with high weights are listed in Table 3.
(For each pattern α the fractional weights are normalized to one as ).
The priors and variance of the patterns of these voxel weights are used to constrain the models at a second hierarchical layer.
Denote D1={d,0} as the weight pattern in which all d weights are not relayed.
Denote W d ={d 1,d 2,d R } as the weight pattern that specifies how d weights are distributed among these channels, where d i is the output weight of the individual codeword transmitted via the channel S i →D or channel R NC →D.
During these many repetitions, a pattern of connection weights is forged that enables the network to respond correctly to each pattern.
In neural networks, a stochastic pattern of synaptic weights is generated after a learning session is completed which can be used as keys.
Once input and weights are normalized, the maximum excitation of a certain output neuron will be achieved when the input exactly matches the weight matrix, providing pattern classification when weights are properly adjusted.
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