面向数据流的多任务多核在线学习算法

Online learning algorithm based on multi-task and multi-kernel for stream data

对于数据流的处理,现有的在线学习算法在准确性上仍有欠缺,故提出一种新的多任务多核在线学习模型用于提高数据流预测的准确性。在保持多任务多核学习的基础上,将其扩展到在线学习中,从而得到一个新的在线学习算法;同时为输入数据保持一定大小的数据窗口,用较小空间换取数据的完整性。实验部分对核函数的选取以及训练样本集的大小进行了较为详细的分析,通过对UCI数据和实际的机场客流量数据进行分析,很好地保障了流数据处理的准确性及实时性,有一定的实际应用价值。

For the prediction of data stream,some online learning algorithms have some shortcomings in accuracy.Therefore,this paper proposed a new multi-task and multi-kernel online learning model to improve the accuracy of data stream prediction.Based on the multi-task multiple-kernel learning,it extended the model to online learning,so as to get a new online learning algorithm,while maintaining a certain size of the input data window for the integrity of the data with less space.In the experimental part,it analyzed the selection of kernel function and the size of training sample set in detail.Through the analysis of UCI data and actual airport passenger flow data,the proposed algorithm can ensure the accuracy and real-time of stream data processing,and has certain applicable value.