… ), K-nearest neighbor (KNN) and SVM in the aspect of precision and sparsity. RVM is implemented
based on Sparse Bayesian V1.1, KNN … A bayesian approach to joint feature selection …

… The evolving contour deforms with a speed F that is based on the contour curvature and
image features like gradient. The curvature component in the speed keeps the contour …

… dimensionality reduction in academia, we choose the feature … two commonly used feature
selection algorithms, SVM-RFE and … , the integrated feature selection method proposed in this …

… approach with alternative approaches like DWT+SVM, DWT+… the hybrid approach reduces
the dimensionality reduction of … the proposed hybrid feature selection approach for detecting …

… based on EEG can be roughly divided into five steps: preprocessing, feature extraction, feature
selection and dimensionality … method: support vector machine(SVM), k-nearest neighbor(…

… 进行选优降维,最后使用支持向量机(SVM),最近邻分类器(KNN)以及随机森林(RF)三种分类器…
the construction of feature matrix, the combination of optimal feature selection algorithm and …

… We compare four typical dimensionality reduction methods in … -SVM method than most of
methods, there are some details we can improve in the future. Gabor wavelets feature selection …

… A new feature selection method for handling redundant information in text classification[J]. …
@article{title="A new feature selection method for handling redundant information in text …

… dimensionality reduction for the data to extract the image … carried on by the classifier of KNN
and SVM. It turns out that the … ap鄄 erture radar images and feature selection in shape space […

… a novel distance based feature extraction method for various … k-NN, and SVM classifiers
show that concatenating the original features provided by the datasets to the distance based …