中文摘要：

针对大规模在线学习问题,提出一种二维分割贯序正则化超限学习机（BP-SRELM）.BP-SRELM以在线贯序超限学习机为基础,结合分治策略的思想,从实例和特征两个维度对高维隐层输出矩阵进行分割,以降低问题求解的规模和计算复杂性,从而极大地提高对大规模学习问题的执行效率.同时,BP-SRELM通过融合使用Tikhonov正则化技术进一步增强其在实际应用中的稳定性和泛化能力.实验结果表明,所提出的BP-SRELM不仅具有更高的稳定性和预测精度,而且在学习速度上优势明显,适用于大规模数据流的在线学习与实时建模.

英文摘要：

To solve the large-scale online learning problem, this paper proposes a bidimensionally partitioned sequential regularized extreme learning machine（BP-SRELM）. Based on the online sequential extreme learning machine, combining the divide-and-conquer strategy, the BP-SRELM partitions a high-dimensional hidden layer output matrix into several small matrices from the aspects of instance dimension and feature dimension, so as to reduce the scale and the complexity of the problem, and consequently, the execution efficiency of the algorithm for large-scale learning problem is significantly improved. Meanwhile, the Tikhonov regularization technology is incorporated in the BP-SRELM to further enhance the stability and the generalization capability of the algorithm in real applications. Experimental results show that the proposed BP-SRELM can provide better performances in the sense of stability and prediction accuracy with greatly improved leaning speed than its counterparts, and it can be applied to the online learning and real-time modeling of large-scale data streams.