论文标题
极限学习机器中多个隐藏节点的有效无逆增量和减少算法
Efficient Inverse-Free Incremental and Decremental Algorithms for Multiple Hidden Nodes in Extreme Learning Machine
论文作者
论文摘要
[4]中提出的无反向极端学习机（ELM）算法是基于一种无反向的算法来计算正则化伪内iNVERSE，该算法是从无反向递归算法推导的，以更新Hermitian Matrix的近相。在[4]中应用递归算法之前，它的改进版本已在先前的文献中使用[9]，[10]。因此，从改进的递归算法[9]，[10]中，提出了几种在[13]中为ELM的几种有效的无反相反算法，以降低计算复杂性。在本文中，我们提出了用Tikhonov正则化的两种无反向算法，用于ELM，这可以增加迭代中的多个隐藏节点。另一方面，我们还提出了用Tikhonov正则化的两种有效的递减学习算法，可以消除迭代中的多个冗余节点。
The inverse-free extreme learning machine (ELM) algorithm proposed in [4] was based on an inverse-free algorithm to compute the regularized pseudo-inverse, which was deduced from an inverse-free recursive algorithm to update the inverse of a Hermitian matrix. Before that recursive algorithm was applied in [4], its improved version had been utilized in previous literatures [9], [10]. Accordingly from the improved recursive algorithm [9], [10], several efficient inverse-free algorithms for ELM were proposed in [13] to reduce the computational complexity. In this paper, we propose two inverse-free algorithms for ELM with Tikhonov regularization, which can increase multiple hidden nodes in an iteration. On the other hand, we also propose two efficient decremental learning algorithms for ELM with Tikhonov regularization, which can remove multiple redundant nodes in an iteration.
