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CLUS-HMC-ENS takes as input vector representations of genes and classifies genes into functional groups by learning an ensemble of decision trees.

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Since the cardinality of ({mathcal {X}}) is small (as is the case for many cohort datasets), we learn an ensemble of models by performing bootstrap sampling over ({mathcal {X}}).

By learning gesture models in an online fashion, the gesture recognition process is made more robust, and the need to train on a large training ensemble is obviated.

By examining neuronal activities at a population level, we revealed that, unlike CA1 neuron populations, dentate granule cell (DGC) ensembles activated by learning were not preferentially reactivated by memory recall.

Random forest is an ensemble algorithm based on learning a collection of decision trees.

Each cluster is then used to learn an HMM, resulting in an ensemble of K HMMs.

By using machine learning techniques like ensemble models and multi-armed bandit methods, Clipper can select and combine predictions from multiple models, even models trained in different machine learning frameworks, to provide more accurate and robust predictions.

Random Forest can be viewed as a machine learning ensemble algorithm and was first proposed by Breiman [ 11].

We introduce the tmChem system, a chemical named entity recognizer created by combining two independent machine learning models in an ensemble.

In our previous study, we proposed an ensemble MIML learning framework EnMIMLNN and design three algorithms for protein function prediction tasks by combining the advantage of three kinds of Hausdorff distance metrics [ 6].

While ordinary classifiers usually try to learn one hypothesis from training data, ensemble learning firstly learns a set of hypotheses and then combines them into an ensemble classifier.

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