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One of the most investigated methods to increase the accuracy of convolutional neural networks (CNN) is by increasing its depth. However, increasing the depth also increases the number of parameters, which makes convergence of back-propagation very slow and prone to overfitting. Convolutional networks with deep supervision (CNDS) add auxiliary branch to addresses the problem of slower convergence...
Least Squares Support Vector Machine (LS-SVM) is a classic algorithm for regression estimation and classification. But unfortunately, for really large problems, LS-SVM can become highly memory and time consuming. In this paper, we present a simplified algorithm for LS-SVM, called ILS-SVM, which effectively reduces the algorithmic complexity. In order to improve the rate of convergence and overcome...
The selection for hyper-parameters is difficult and important to the performance of least squares support vector machines (LS-SVM). The existed parameters selection algorithms, such as the analytical, algebraic techniques and particle swarm optimization (PSO) algorithm, have their own shortcomings. In this paper, the problem of model selection for LS-SVM is discussed and a new method selecting the...
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