Sentence examples for been employed to classify from inspiring English sources

To this end, support vector machines (SVMs) [23], nearest-neighbor (NN) classifiers, Gaussian mixture model-based ones [3], and classifiers relying on sparse and low-rank representations [24] have been employed to classify the audio features into genre or mood classes.

Several typing methods have been employed to classify isolates with differing degrees of success [12].

Several strategies have been employed to classify expression profiles based on exposure to chemicals of known mechanisms of action [ 19, 20].

Among them, BMI and WC are the main criteria that have been employed to classify obesity and WHR has recently been suggested to be more significantly associated with the risk of cardiovascular events than WC [ 8].

In addition to the odds ratio, oligonucleotide frequencies (as n-mers) and machine learning methods have been employed to classify sequences based on their organism of origin [ 1, 3- 20], and to identify unique features of genomic data sets.

Several different types of supervised analysis have been employed to classify spectral data from cells or tissues, including linear discriminant analysis (LDA) [ 26- 28], metric bayesian classification [ 29- 31], support vector machines (SVM) [ 32, 33] and artificial neural networks (ANN) [ 34- 37].

Two Neural Network (NN) classifiers, four Support Vector Machine (SVM) classifiers and K-Nearest Neighbor (KNN) classifier are employed to classify the ECG signals and compared with the proposed classifier.

An HMM classifier was employed to classify each video frame in the arousal-valence axis.

Next, a standard, supervised image classification approach (i.e. Maximum Likelihood Classifier) was employed to classify two major land covers of interest: agricultural land and settlement (urban) areas.

Supervised methods, such as Bayesian classifiers, decision trees and support vector machines, are employed to classify compounds for a particular functional activity class.

For example, when diagnosing cancers based on gene expression profiles, low accuracy is expected if a traditional classifier, for example, linear discriminant analysis (LDA), is employed to classify the samples based on all the genes measured.