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In the paper, a regularized correntropy criterion (RCC) for radial basis function neural network (RBFNN) is proposed. In RCC, the Gaussian kernel function is used to replace the Eculidean norm of the sum-squared-error (SSE) criterion. Replacing SSE by RCC can improve the anti-noise ability of RBFNN. Moreover, the optimal weights and the optimal bias terms can be iteratively obtained by the half-quadratic...
Learning an optimal kernel plays a pivotal role in kernel-based methods. Recently, an approach called optimal neighborhood kernel learning (ONKL) has been proposed, showing promising classification performance. It assumes that the optimal kernel will reside in the neighborhood of a “pre-specified” kernel. Nevertheless, how to specify such a kernel in a principled way remains unclear. To solve this...
The paper introduces the random factor in Particle Swarm Optimization. Comparing with inertia weight, the particle's velocity is determined by previous velocity, own experience, public knowledge and random behavior. The random operator is similar with the mutation operator in the Genetic Algorithms. Simulation results show that the method introducing the random factor is better than inertia weight...
Analyzing the distance between the location and the new location, we conclude inertia weight method which linearly decreases from 0.9 to 0.4 has the powerful local search ability on Schafferpsilas F6 function. In order to improve the balance between local and global search ability, the novel adaptive PSO algorithm which evaluates a reset function to control the inertia weight value is proposed. Once...
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