文件名称:SVM
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In this paper, we show how support vector machine (SVM) can be
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is introduced by
using the SVM. The kernel mapping idea is used to derive the
non-linear version, Kernel Discriminant via Support Vectors (SVKD).
In SVDA, only support vectors are involved to obtain the
transformation matrix. Thus, the computational complexity can be
greatly reduced for kernel based feature extraction. Experiments
carried out on several standard databases show a clear improvement
on LDA-based recognition
employed as a powerful tool for $k$-nearest neighbor (kNN)
classifier. A novel multi-class dimensionality reduction approach,
Discriminant Analysis via Support Vectors (SVDA), is introduced by
using the SVM. The kernel mapping idea is used to derive the
non-linear version, Kernel Discriminant via Support Vectors (SVKD).
In SVDA, only support vectors are involved to obtain the
transformation matrix. Thus, the computational complexity can be
greatly reduced for kernel based feature extraction. Experiments
carried out on several standard databases show a clear improvement
on LDA-based recognition
相关搜索: knn
SVM
MATLAB
svm
knn
feature
transformation
feature
reduction
LDA
SVM
kernel
knn
feature
k-t
knn
classifier
matlab
linear
classifier
SVM
MATLAB
svm
knn
feature
transformation
feature
reduction
LDA
SVM
kernel
knn
feature
k-t
knn
classifier
matlab
linear
classifier
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下载文件列表
Nouveau dossier\oneoutsvnn.m
...............\SVDA.m
...............\SVDA_example.m
Nouveau dossier
...............\SVDA.m
...............\SVDA_example.m
Nouveau dossier