Quadratic programming is the process of solving a special type of mathematical optimization problem. Recent advances in online solutions for quadratic programming problems (QPPs) have created opportunities to widen the scope of applications for support vector regression (SVR). In this vein, efforts to make SVR compatible with streaming data have been met with substantial success. However, streaming data with concept drift remain problematic because the trained prediction function in SVR tends to drift as the data distribution drifts. Aiming to contribute a solution to this aspect of SVR’s advancement, we have developed continuous SVR (C-SVR) to solve regression problems with nonstationary streaming data, that is, data where the optimal input–output prediction function can drift over time. The basic idea of C-SVR is to continuously learn a series of input–output functions over a series of time windows to make predictions about different periods. However, strikingly, the learning process in different time windows is not independent. An additional similarity term in the QPP, which is solved incrementally, threads the various input–output functions together by conveying some learned knowledge through consecutive time windows. How much learned knowledge is transferred is determined by the extent of the concept drift. Experimental evaluations with both synthetic and real-world datasets indicate that C-SVR has better performance than most existing methods for nonstationary streaming data regression.

中文翻译：

---

非平稳流数据的连续支持向量回归


二次规划是解决特殊类型的数学优化问题的过程。二次规划问题 (QPP) 在线解决方案的最新进展为扩大支持向量回归 (SVR) 的应用范围创造了机会。在这方面，使 SVR 与流数据兼容的努力已经取得了巨大的成功。然而，具有概念漂移的流数据仍然存在问题，因为 SVR 中经过训练的预测函数往往会随着数据分布的漂移而漂移。为了为 SVR 的这方面进步提供解决方案，我们开发了连续 SVR (C-SVR) 来解决非平稳流数据的回归问题，即最佳输入输出预测函数可能随时间漂移的数据。 C-SVR的基本思想是在一系列时间窗口上不断学习一系列输入输出函数，以对不同时期进行预测。然而，引人注目的是，不同时间窗口的学习过程并不是独立的。 QPP 中的一个额外的相似性项是增量解决的，通过通过连续的时间窗口传达一些学到的知识，将各种输入输出函数连接在一起。迁移多少学到的知识取决于概念漂移的程度。对合成数据集和真实数据集的实验评估表明，C-SVR 比大多数现有的非平稳流数据回归方法具有更好的性能。