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Support vector machines (SVMs) is a statistical learning method with good performance when the sample size is small, due to their excellent performance, SVMs are now used extensively in pattern classification applications and regression estimation, Unfortunately, it is currently considerably slower in test phase caused by number of the support vectors, which has been a serious limitation for some...
Fault redundancy information can increase computation complexity and reduce the precision of fault diagnosis. Feature extraction becomes very important to improve the performance of fault diagnosis. A supervised kernel learning algorithm based on manifold is presented to carry out feature extraction. The proposed algorithm firstly implements locality preserving projection in reproducing kernel Hilbert...
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