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Then a support vector machine was trained using the nine dataset to correlate the catalyst preparation parameters and the kinetic parameters.

A learning machine was trained using the Random Forest algorithm with prediction accuracy assessed using an out-of-bag estimation ("randomForest" package in R).

The machine was trained with this data and predicted quite accurately the experimental outcomes.

In each of the validation steps, a linear support vector machine was trained on the data of all but one subject and tested on the remaining one.

A support vector machine was trained using the average upstream distance of the identified motifs from the translation start site on both strands of binding sites.

In this step, a Support Vector Machine was trained based on the survey results as training data, and the number of links to common databases (e.g. links to abstracts in PubMed or domains in Pfam).

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The Eve machine is trained posing as either the Alice machine or the Bob machine.

The Restricted Boltzmann Machine is trained in such a way that the obtained model parameter (varTheta) should minimize the negative log-likelihood of the given training data set.

The ensembles of Artificial neural network and Support vector machine were trained with the Eigen Colour feature to classify product images acquired from the PI100 corpus into 100 classes and their classification accuracies were compared.

A relevance vector machine is trained over 3 dimensional feature vectors pertaining to global, local and rarity measures of conspicuity, to yield probabilistic values which form the saliency map.

A support vector machine is trained to classify every pixel of a volume for its position above or below certain boundaries.

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