抗离群值的鲁棒正则化贯序超限学习机

针对离群值环境下的在线学习问题,提出一种鲁棒正则化贯序超限学习机(Robust regularized online sequential extreme learning machine,RR-OSELM)。RR-OSELM以增量学习新样本的方式实现在线学习,并在学习过程中基于样本的先验误差进行逆向加权计算以降低学习模型对于离群值的敏感性;同时RR-OSELM通过融合使用Tikhonov正则化技术进一步增强了其在实际应用中的稳定性。实验结果表明,RR-OSELM具有较同类算法更好的鲁棒性和实用性,对于离群值环境下的在线建模与预测问题是积极有效的。

Robust Regularized Online Sequential Extreme Learning Machine for Outliers Restraining

Aiming at the online learning with outliers, this paper proposes a robust regularized online sequential extreme learning machine (RR-OSELM). The proposed RR-OSELM is able to learn the newly arrived samples incrementally by a recursive fashion, and assign inverse weights for each example based on the priori error so as to reduce its sensibility to outliers. The Tikhonov regularization technique is incorporated in the RR-OSELM to further enhance the stability of the algorithm in real applications. Experimental results show that the proposed RR-OSELM is more robust than its counterparts, and it can be applied to the online modeling and prediction of data streams with outliers.