Sentence examples for model for feature from inspiring English sources

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.

An example of such connectivity is represented by the ring model of Hansel and Sompolinsky [48], which is a well-known model for feature selectivity in primary visual cortex.

Experimental studies are carried out using vibration signals to verify the effectiveness of the DBN model for feature learning, providing a new way of feature extraction for automatic fault diagnosis in manufacturing.

Further, we account for estimation errors in the feature extraction process that we consider as outliers, thus arriving at the following observation model for feature vectors at time instant n, n=1,…,N: boldsymbol{d}_{jkn}= boldsymbol{w}_{k} +boldsymbol{e}_{jkn}+ boldsymbol{o}_{jn}.

measures of 78.4% and 79.7% were achieved with the 6 state model for feature sets H and J. Checking the Viterbi recognition results from the training data revealed a mismatch in performance for the test data and training data, which in turn prompted a reevaluation of the video material.

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.