Sentence examples for matrices for classification from inspiring English sources

Table 5 shows the confusion matrices for classification results using 15 features extracted from different T-F representations of EEG segments of length T = 11.8 s (N = 2,048 samples).

Table 2 Confusion matrix for classification rates of the three human motions collected at incidence angle.

Classifier Average classification rate Linear classifier 77.4% MLP classifier 85.5% SVM classifier 80.9% Table 5 Confusion matrix for classification rates of three human motions at using a MLP as classifier in Stage 3 of HICA.

Table 2 shows the confusion matrices for different classification procedures.

This paper also introduces efficient techniques of temporal compaction, which operate directly on kernel matrices for kernel classification algorithms such as the support vector machine (SVM).

The results indicate that class F signals are most often confused with class H and S signals; likewise class S with class F. Table 5 Confusion matrices for EEG classification using T-F features set {FS 1, FS 2,..., FS 10, FI 1,..., FI 5} extracted from the TFD with SVM classifier and Neural Network-based classifier.

Interactome data from secondary data sources were encoded in a graph-based way and used in similarity matrices for patient classification.

We design an improved discriminative common vector by adjustment for the Fisher criterion that can estimate the within-class and between-class scatter matrices more accurately for classification purposes.