Reinforced Extreme Learning Machines for Fast Robust Regression in the Presence of Outliers
提出一种基于逐点概率强化方法的极限学习机稳健推理算法，在存在异常值时仍能快速获得与前沿方法相当的回归结果。
Extreme learning machines (ELMs) are fast methods that obtain state-of-the-art results in regression. However, they are not robust to outliers and their meta-parameter (i.e., the number of neurons for standard ELMs and the regularization constant of output weights for L2 -regularized ELMs) selection is biased by such instances. This paper proposes a new robust inference algorithm for ELMs which is based on the pointwise probability reinforcement methodology. Experiments show that the proposed approach produces results which are comparable to the state of the art, while being often faster.