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The algorithm uses a training dataset to learn patterns and finally use those patterns to classify new cases.
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We use the maximum likelihood estimator for each attribute using a training dataset.
Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it.
It "teaches" a machine to solve a problem using a training dataset; i.e. a set of input data with corresponding, labelled output data.
The approach feasibility is demonstrated by using a training dataset of computer tomography scans from British subjects aged 25 106 years (75 male and 34 female).
Many action recognition and 3D body tracking works rely on proper models of human motion, which constrain the search space using a training dataset of prerecorded motions [7 10].
Using a training dataset of 1500 cases (examinees) and 45 test items, the proposed approach automatically selected only 12 items which classified an evaluation population of 500 cases with 91% accuracy.
One recent interesting approach is to use a training dataset to inform how respiratory motion can be gated using list mode PET data itself, although this requires a short acquisition after the completion of the study [5].
For example, FlashDetect's machine learning classifier was evaluated using a training dataset comprising only 47 samples of each class, but even this small sample size was enough to achieve a high detection accuracy.
This module extracts the probability π of starting at the word ω and the probability P(ω j |ω i ) of transitioning from the word ω i to the word ω j using a training dataset of sentences assigned to the motion symbols.
The ANN system (see the Methods section for further details) was evaluated using a training dataset containing 1074 peptides.
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