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- W2354567940 abstract "More and more difficulties are encountered when using traditional algorithms (including MD, ML, NNC, RBFNNC) to classify high dimension multi-spectral image. In order to overcome these limitations, support vector machine (SVM) is used to classify the high dimension image. The Hughes phenomenon is mitigated greatly, which is notorious in classification of high dimension data with limited training samples by traditional classifier. So the classification accuracy of SVM is better than traditional ones. The relationship between the classification performance of SVM and training set, data dimension is studied. Experiment results show that the generalization ability of SVM is strong, and its classification accuracy is better than traditional algorithms, no matter whether the training set is huge or small. Advantages of SVM are much more notable when the training set is small and the data dimension is high." @default.
- W2354567940 created "2016-06-24" @default.
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- W2354567940 date "2003-01-01" @default.
- W2354567940 modified "2023-09-26" @default.
- W2354567940 title "High Dimensional Multi-spectral Image Classification by SVM and Its Characteristic Analysis" @default.
- W2354567940 hasPublicationYear "2003" @default.
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