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Feature reduction is an effective way to improve the classification performance when machine learning methods are used in gait analysis. In this paper, we proposed a novel hybrid feature reduction method (MSNR&PCA) based on the combination of feature ranking with principle component analysis (PCA). Three feature reduction methods, namely, feature ranking based the value of signal to noise ratio...
This article presents an automatic color‐based feature extraction system for parameter estimation of oral cancer from optical microscopic images. The system first reduces image‐to‐image variations by means of color normalization. We then construct a database which consists of typical cancer images. The color parameters extracted from this database are then used in automated online sampling from oral...
The effect of PC (principal component) number upon SAR ATR (synthetic aperture radar automatic target recognition) performance based on PCA (principal component analysis) is analyzed. First, PCA features are extracted with different PC number, and then SVM is used to classify. Experimental results based on MSTAR data sets show that the performance is optimized when the accumulative contribution rate...
In this paper, Adaboost and SVM are applied to SAR ATR (synthetic aperture radar automatic target recognition) respectively. The performance of these two classifiers is analyzed and compared in target aspect window with different size. First, PCA (principal component analysis) features are selected as target feature, and then Adaboost.Ml and SVM are used to classify, respectively. Experimental results...
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