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Input: dimensions of the DWT domain subband and smoothing parameters.
We analyze its parallel efficiency and scalability toward very large input dimensions.
Clearly, the presented four input dimensions do not completely cover the entire parameter space of the problem at hand.
Simple MRM variants appear quite promising in low input dimensions with highly efficient computation of a bounding established with certainty.
In CNN, the number of free parameters does not grow proportionally with the input dimensions and therefore performs better in terms of many benchmarks.
For one, we may need heuristic criteria which could select from a large number of parameters (e.g., input dimensions, number of clusters, distance thresholds, etc).
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Thus, input dimension size is reduced and network becomes smaller.
Thus, the input dimension size is reduced and network becomes smaller.
Prediction accuracy can be improved using suitable input dimension and time delay.
However, the number of simulations required to learn a generic multivariate response grows exponentially as the input dimension increases.
Algorithm for input dimension reduction is first formulated and then applied to real ECG data for recognition of beat patterns.
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