Support Vector Machine is a very good solution to the classification problem of small sample, but when theinput feature vector dimension is larger, the classifier has complex structure, long training time and degradedperformance. In order to solve this problem, a feature extraction method based on wavelet coefficients cluster was putforward. All the wavelet coefficients was clustered, the energy value of wavelet coefficients in each cluster was calculatedand used as the input feature vector of a classifier. The dimension of input data was reduced greatly, and at the same timethe specific problem information was retained. Support Vector Machine was used to identify the defects in steel plate, theexperiment results showed that the method has higher classification accuracy.
展开▼
