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Dynamic State Estimation (DSE) for power system considers statistical characters of systemic state variables in past period, has functions of state estimation and forecasting. This paper proposes a new method for state estimation problem in power systems based on Kernel Principle Component Analysis (KPCA) and Support Vector Regression (SVR). Firstly, the KPCA can extract the nonlinear relationship...
We present an algorithm for detecting human actions based upon a single given video example of such actions. The proposed method is unsupervised, does not require learning, segmentation, or motion estimation. The novel features employed in our method are based on space-time locally adaptive regression kernels. Our method is based on the dense computation of so-called space-time local regression kernels...
A new feature extraction method for high dimensional data using least squares support vector regression (LSSVR) is presented. Firstly, the expressions of optimal projection vectors are derived into the same form as that in the LSSVR algorithm by specially extending the feature of training samples. So the optimal projection vectors could be obtained by LSSVR. Then, using the kernel tricks, the data...
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