论文标题
用多个内核分发在线学习
Distributed Online Learning with Multiple Kernels
论文作者
论文摘要
在The Internet（IoT）系统中，有大量的IoT设备（例如传感器）提供了大量信息数据。从这些数据中学习功能对物联网系统的机器学习任务非常感兴趣。专注于流媒体（或顺序）数据，我们提出了一个具有多核心（命名domkl）的分布式在线学习框架的分布式在线学习框架。提出的DOMKL是通过利用乘数（OADMM）和分布式树篱算法的在线交替方向的原理来设计的。从理论上讲，我们证明，在T时间插槽中，Domkl可以实现最佳的sublerear遗憾，这意味着每个学识渊博的功能都可以在事后获得最佳功能的性能，就像最先进的集中在线学习方法中一样。此外，可以确保随着T的成长，即所谓的共识约束，任何两个相邻学习者的学识渊博的功能都具有微不足道的差异。通过具有各种实际数据集的实验测试，我们验证了拟议DOMKL对回归和时间序列预测任务的有效性。
In the Internet-of-Things (IoT) systems, there are plenty of informative data provided by a massive number of IoT devices (e.g., sensors). Learning a function from such data is of great interest in machine learning tasks for IoT systems. Focusing on streaming (or sequential) data, we present a privacy-preserving distributed online learning framework with multiplekernels (named DOMKL). The proposed DOMKL is devised by leveraging the principles of an online alternating direction of multipliers (OADMM) and a distributed Hedge algorithm. We theoretically prove that DOMKL over T time slots can achieve an optimal sublinear regret, implying that every learned function achieves the performance of the best function in hindsight as in the state-of-the-art centralized online learning method. Moreover, it is ensured that the learned functions of any two neighboring learners have a negligible difference as T grows, i.e., the so-called consensus constraints hold. Via experimental tests with various real datasets, we verify the effectiveness of the proposed DOMKL on regression and time-series prediction tasks.
