Sentence examples for a model for classification from inspiring English sources

We present a model for classification rules designed to use both non-temporal attributes and sequences of temporal events as predicates.

Further, a model for classification of multi-carrier signals is presented, including Gaussianity, cyclostationarity, and autocorrelation tests for further extracting orthogonal frequency division multiplexing signal parameters.

MetaVelvet-SL utilizes LIBSVM to develop a model for classification of chimeric nodes.

In 1992 a model for classification of surgical complications was proposed by Clavien and colleagues [ 10].

Four of these parts were input into the RF to establish a model for classification, and every instance of the remaining part was predicted by the model.

The next step is BLR (Bayesian Logistic regression) classification, which is a recursive training process to select best classification parameters to fit the input features and generate a model for classification.

Since MetaVelvet-SL needs to learn a model for the classification of chimeric nodes, we have provided a pipeline connecting MetaPhlAn and MetaVelvet-SL, which can generate a classification model and assemble automatically.

In this sense, this work proposes a model for automatic classification of urban soundscapes.

The expected behaviour of the response variable should be considered when choosing a model for multiclass classification.

MetaVelvet-SL consists of two main modules: first, the supervised learning module to develop a model for the classification of chimeric nodes and, second, the assembly module.

In the implementation, MetaVelvet-SL consists of two main modules: (i) the supervised learning module to develop a model for the classification of chimeric nodes and (ii) the assembly module.