K插值单纯形法核极限学习机的研究

针对核极限学习机高斯核函数参数选优难，影响学习机训练收敛速度和分类精度的问题，该文提出一种K插值单纯形法的核极限学习机算法。把核极限学习机的训练看作一个无约束优化问题，在训练迭代过程中，用Nelder-Mead单纯形法搜索高斯核函数的最优核参数，提高所提算法的分类精度。引入K插值为Nelder-Mead单纯形法提供合适的初值，减少单纯形法的迭代次数，提高了新算法的训练收敛效率。通过在UCI数据集上的仿真实验并与其它算法比较，新算法具有更快的收敛速度和更高的分类精度。

Kernel Extreme Learning Machine Based on K Interpolation Simplex Method

The kernel Extreme Learning Machine (ELM) has a problem that the kernel parameter of the Gauss kernel function is hard to be optimized. As a result, training speed and classification accuracy of kernel ELM are negatively affected. To deal with that problem, a novel kernel ELM based on K interpolation simplex method is proposed. The training process of kernel ELM is considered as an unconstrained optimal problem. Then, the Nelder-Mead Simplex Method (NMSM) is used as an optimal method to search the optimized kernel parameter, which improves the classification accuracy of kernel ELM. Furthermore, the K interpolation method is used to provide appropriate initial values for the Nelder-Mead simplex to reduce the number of iterations, and as a result, the training speed of ELM is improved. Comparative results on UCI dataset demonstrate that the novel ELM algorithm has better training speed and higher classification accuracy.