Sentence examples for advanced recognition of from inspiring English sources

Such ideas are all important to the advanced recognition of a distressful incident often endangering countless lives and causing substantial economic and social damage.

Similarly, when 60 samples are used as training samples, all of these samples are used to train the classical SVM, while there are 36 training samples for the SVM in the advanced recognition of the hybrid recognition, i.e., m = 60 and m ′ = 36.

The time complexity of SVM is O ( m 3 ), and that of the proposed approach is O ( m ′ 3 ), where m and m ′ are the number of training samples for the SVM and the number of training samples for the SVM in the advanced recognition of the hybrid recognition, respectively.

where Atotal is the accuracy of the hybrid recognition, Aprimary is the accuracy of the primary recognition, Aadvanced is the accuracy of the advanced recognition, NWIU is the number of samples which are falsely classified in uncertain area, and N W is the number of wrong classified samples.

The time complexity of the approach proposed in this article consists of two parts, namely the time complexity of the primary recognition and the time complexity of the advanced recognition.

In conclusion, the time complexity of our hybrid recognition is O ( dm ) + O ( m ′ 3 ), where m ′ denotes the number of training samples for SVM in the advanced recognition (After the primary recognition, the training samples for SVM is reduced).

The advanced recognition accuracy is 92%.

In this section, a hybrid radar emitter recognition approach that consists of a rough k-means classifier in the primary recognition and a SVM classifier in the advanced recognition is proposed.

The uncertain sample set, which contains most of the samples with linear inseparability, is classified by the SVM in the advanced recognition.

The samples in the margin of a cluster are picked up to be used as the training data for the SVM in the advanced recognition.

The hybrid recognition approach is made up of two classifiers, a linear classify and a nonlinear classify, which can classify linearly separable samples and pick up those linearly inseparable samples to be classified in the advanced recognition using SVM.