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If training input points and models are simultaneously optimized, the generalization performance would be further improved.
The geometrical properties of the tubular joints were used as the training input data.
We achieved a 10 percentage-point improvement in event classification accuracy, with a 200x reduction in the number of training input examples as compared to using the entire track.
Optimally designing the location of training input points (active learning) and choosing the best model (model selection)–which have been extensively studied are two important components of supervised learning.
The trace collection can be viewed as the training input for this model.
Note that this strategy does not prevent future studies from including visual information as additional training input.
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That is because other data used as training inputs might correlate with being blue or purple.
Training inputs for the sound texture synthesis algorithm are selected from the Ultimate Fire sound library.
Several input configurations are tested, including, reduced sets of training inputs (analysis runs), training inputs of high and low fidelity, two sampling processes for these inputs (i.e., random and stratified sampling) and two different IM representations.
Time series of the translator position along some sinusoidal trajectories were selected as training inputs.
However, they generalize poorly at locations far from their training inputs, making those predictions have high variance (low precision).
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