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Time-based Spiking Neural Network (SNN) has recently received increased attentions in neuromorphic computing system designs due to more bio-plausibility and better energy-efficiency. However, unleashing its potentials in realistic cognitive applications is facing significant challenges such as inefficient information representations and impractical learnings. In this work, we aim for exploring a practical...
In this paper, we propose a l2,1-norm based discriminative robust transfer learning (DKTL) method for domain adaptation tasks. The key idea is to simultaneously learn discriminative subspaces by using the proposed domain-class-consistency (DCC) metric, and the representation based robust transfer model between source domain and target domain via l21-norm minimization. The DCC metric includes two parts:...
We combine the training and testing stages of support vector regression into a filtering process. Then we prove that the least squares support vector regression (LS-SVR) based on the translation invariant kernel is a linear time-invariant system. And we find that the common radial basis function kernel-based LS-SVR has properties of lowpass and linear phase filter in the applications to signal processing...
When it is hard to obtain training samples, the fault classifier based on support vector machine (SVM) can diagnose faults with high accuracy. It can easily be generalized and put to practical use. In this paper, a fault classifier based on support vector machine (SVM) is proposed for analog circuits. It can classify the faults in the target circuit effectively and accurately. In order to test the...
Image-based spam is becoming a new threat to the Internet and its users. In our early work, we proposed an image filtering system which detects the spam image by matching with user-specified image content using SIFT algorithm. In order to further improve efficiency, we develop a quick image matching algorithm instead of SIFT. After using difference-of-Gaussian to extract image feature points, we adopt...
Support vector machines (SVM) is a machine-learning algorithm based on statistical learning theory. The method for power transformer fault diagnosis based on SVM is proposed in this paper. The principle and algorithm of this method are introduced. Through a finite learning sample the relation is established between the transformer fault signature and the quantity of its dissolved gas. A faults classifier...
In order to identify the rotating machinery fault, a method based on support vector machine (SVM) is proposed in this paper. After the feature vectors from the fault signals by means of wavelet packet are extracted and the support vector machine (SVM) classification algorithm to the classification of faults in rolling bearing is applied. By drawing a comparison between the classification and BP neural...
Based on the comprehensive analysis of the existing risk early warning methods in real estate, a new risk forecast method based on support vector machine is put forward. And a risk early warning model in real estate market based on support vector machine is established. The realization process of the risk early warning method is discussed. Taking the practical data in real estate exploitation as the...
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