The phrase "are employed to classify" is correct and usable in written English.
It can be used in contexts where you are discussing methods, tools, or systems that are used for categorization or classification purposes.
Example: "Various algorithms are employed to classify the data into distinct categories for better analysis."
Alternatives: "are used to categorize" or "are utilized for classification".
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.
Supervised methods, such as Bayesian classifiers, decision trees and support vector machines, are employed to classify compounds for a particular functional activity class.
Three neural network classifiers: Back Propagation Network (BPN), Feed Forward Network (FFN) and Multilayered Perceptron (MLP) are employed to classify the ECG signal.
Partial least squares regression (PLSR), linear discriminant analysis (LDA) and artificial neural network (ANN) respectively are employed to classify the data.
A probabilistic neural network (PNN), a neural network implementation of multivariate Bayesian classification scheme, and a heuristic classification algorithm are employed to classify vehicle types.
To test the performance of the final subset of features, classifiers including linear discriminant analysis (LDA) and support vector machine (SVM) are employed to classify the reduced feature set of the two groups.
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.
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.
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.
