Sentence examples for models for features from inspiring English sources

Two sets of conservation features were developed from this: A static set of 77 distribution models for features which ignored seasonal dynamics, and a seasonal set of 147 distribution models which included seasonal dynamics.

Moreover, there are researchers trying to combine multiple features by well-designed models for feature fusion.

In the training stage, DAE and BF-DNN models for feature transformation and speaker models with transformed features are trained.

In the test stage, first, MFCCs extracted from the reverberant speech are input to the DAE and BF-DNN models for feature transformation.

This paper introduces two hybrid models, i.e. PCA with bagging and PCA with Bayesian boosting models for feature based opinion classification of product reviews.

In what follows, we introduce two Bayesian models for feature selection in high-dimensional data, specifically designed for the purpose of classification.

As the problem discussed in this study is centered on models for feature selection, the second stage was performed several times as the purpose was to iteratively search for the optimal parameters for a model or for establishing the minimal number of variables necessary for adequately fitting a model without losing its predictive power.

Although we use linear models for feature selection and classification, none of our reported estimates of accuracy depend upon whether or not a linear model holds strictly.

The flow modeling for features such as ribs and tapered sections are validated independently before integrating them into the mold geometry.

In this paper, we construct a novel rough set model for feature subset selection.

Owing to high computational efficiency and nonlinear projection ability of using kernel functions, kernel methods are applied to extend the traditional linear model for feature selection and fusion.