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A distributed online learning framework for support vector machines (SVMs) is presented and analyzed. First, the generic binary classification problem is decomposed into multiple relaxed subproblems. Then, each of them is solved iteratively through parallel update algorithms with minimal communication overhead. This computation can be performed by individual processing units, such as separate computers...
We propose a novel non-parametric adaptive outlier detection algorithm, called LPE, for high dimensional data based on score functions derived from nearest neighbor graphs on n-point nominal data. Outliers are predicted whenever the score of a test sample falls below ??, which is supposed to be the desired false alarm level. The resulting outlier detector is shown to be asymptotically optimal in that...
In this paper, an improved Levenberg-Marquardt learning algorithm based on terminal attractors for feedforward neural networks is proposed. The effectiveness of the proposed algorithm in improving learning speed is shown by the simulation results.
When applying traditional methods to train approximately linear support vector machine (SVM), we will get a kernel matrix which occupy mass computer memory and lead a slow convergence speed. In order to improve the convergence speed of SVM, a method of training approximately linear support vector machine based on variational inequality (VIALSVM) was proposed. The method turns the convex quadratic...
This paper proposes a wavelet neural networks (WNN) with self-adaptive learning rate. The algorithm can automatically change the learning rate with operational parameter, but without any artificial adjustments. Thus it once for ado overcomes the drawbacks of WNN, i. e. slow convergence, inability to determine the value of learning rate and easiness to fall into local minimum point. The results of...
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