Sentence examples for long training process from inspiring English sources

Exact(8)

However, neural networks have been criticized for its long training process since the optimal configuration is not known a priori.

However, the relative importance of potential input variables and the long training process have often been criticized and hence limited its application in handling classification problems.

In addition, the recognized difficulty of long training process is overcome by using SDBCC algorithm, a novel neural network design method combining structure decomposition and the Cascade-Correlation neural network algorithm.

However, the decision of network's topology, importance of potential input variables and the long training process has often long been criticized and hence limited its application in handling credit scoring problems.

"It's going to have to be a patient, long, training process.

Critical to attracting young people into scientific careers is ensuring that there are a "sufficient number of permanent positions at the end of the long training process," said Daniela Corda, rapporteur for working group D. But, she highlighted, that does not necessarily mean only traditional, hands-on research roles.

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Similar(52)

Nevertheless, neural networks have been criticized for their long trial-and-error training process since the optimal configuration is not known a priori.

For color images with three color channels (RGB), the intensities from all the color channels can be combined into one long vector in the training process.

Although the training process was long, hard and challenging, Matt kept going.

17 20 Despite the well-documented epidemiology injury profile in taekwondo competition, relatively few studies have evaluated the incidence of injury risk factors related to the training process and long-term preparation in elite level athletes.

One reason is that the backpropagation algorithm, which is used to train artificial neural networks, usually starts from a random weight initialization which complicates the optimization process leading to long training times and increases the risk of stopping in a poor local minima.

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