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Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of...
The load curve presents certain randomness for reasons such as human social activities, the load of electric vehicle charging and discharging and so on, which covers up the regularity of load sequence. This paper proposes an approach which can restore the original feature of the loads. In this approach, firstly, the bad data is excluded; Secondly, the characteristics of the time series are extracted...
Most of the modern hard disk drives support Self-Monitoring, Analysis and Reporting Technology (SMART), which can monitor internal attributes of individual drives and predict impending drive failures by a thresholding method. As the prediction performance of the thresholding algorithm is disappointing, some researchers explored various statistical and machine learning methods for predicting drive...
In nowadays, fault diagnosis method for analog circuit based on support vector machines, has become a hot topic in research field of fault diagnosis. However, in practical application of this method, the imbalanced problem occurred in fault sample dataset has greatly influenced its effectiveness. To remedy this problem, this paper proposed an improved Support Vector Machines method based on biased...
E-mail is a major revolution taking place over traditional communication systems due to its convenient, economical, fast, and easy to use nature. A major bottleneck in electronic communications is the enormous dissemination of unwanted, harmful emails known as spam emails. In this paper, a novel spam filtering framework (NSFF) is proposed, which is based on particle swarm optimization, fuzzy logic...
In the conventional regularized learning, training time increases as the training set expands. Recent work on L2 linear SVM challenges this common sense by proposing the inverse time dependency on the training set size. In this paper, we first put forward a Primal Gradient Solver (PGS) to effectively solve the convex regularized learning problem. This solver is based on the stochastic gradient descent...
It is an extreme challenge to produce a nonlinear SVM classifier on very large scale data. In this paper we describe a novel P-packSVM algorithm that can solve the support vector machine (SVM) optimization problem with an arbitrary kernel. This algorithm embraces the best known stochastic gradient descent method to optimize the primal objective, and has 1/?? dependency in complexity to obtain a solution...
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