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As I read this article I thought of a few sunday school lessons that were taught to me as a young boy, so it's interesting to see someone else with the same train of thought.
The d(n) is certainly obtained with the same train sequence.
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Five neural networks with different network structures were trained with the same training data.
This method is used to create an SVM architecture that yields on average a higher accuracy than a traditional soft margin SVM trained with the same training set.
No. Services can run much faster than they do now but not with the same trains or track as HS2 – think 125mph maximum rather than 225mph.
We use five classical modelling methods (index, weight-of-evidence, logistic regression, decision tree, and unique condition unit) with the same training set but with different architectures of input data made up of controlling factors.
However, different performing ANNs might be obtained with the same training data as a result of the random initialization of the weight parameters in each of the network, leading to an uncertainty in selecting the best performing ANN.
On the basis that the same principle can be applied to feature selection, we describe two approaches: (i) homogeneous, i.e., using the same feature selection method with different training data and distributing the dataset over several nodes; and (ii) heterogeneous, i.e., using different feature selection methods with the same training data.
The reason this principle works against building efficient networks is that the world is organized by like things people with the same training tend to be in the same department, just as people with similar backgrounds tend to live in the same neighborhood.
The compact acoustic model containing the CGs is trained with the same training data by following the training procedure outlined in Section 4.2.
For fair comparison, different machine learning methods were trained with the same training sets and tested with the same testing sets.
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