2014 OnlineMultipleKernelRegression

• (Sahoo et al., 2014) ⇒ Doyen Sahoo, Steven C.H. Hoi, and Bin Li. (2014). “Online Multiple Kernel Regression.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623712

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Online+Multiple+Kernel+Regression
• http://dl.acm.org/citation.cfm?id=2623330.2623712&preflayout=flat#citedby

Quotes

Author Keywords

• Concept learning; kernel regression; multiple kernel learning; online learning; time series prediction

Abstract

Kernel-based regression represents an important family of learning techniques for solving challenging regression tasks with non-linear patterns. Despite being studied extensively, most of the existing work suffers from two major drawbacks: (i) they are often designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient and but also poorly scalable in real-world applications where data arrives sequentially; and (ii) they usually assume a fixed kernel function is given prior to the learning task, which could result in poor performance if the chosen kernel is inappropriate. To overcome these drawbacks, this paper presents a novel scheme of Online Multiple Kernel Regression (OMKR), which sequentially learns the kernel-based regressor in an online and scalable fashion, and dynamically explore a pool of multiple diverse kernels to avoid suffering from a single fixed poor kernel so as to remedy the drawback of manual / heuristic kernel selection. The OMKR problem is more challenging than regular kernel-based regression tasks since we have to on-the-fly determine both the optimal kernel-based regressor for each individual kernel and the best combination of the multiple kernel regressors. In this paper, we propose a family of OMKR algorithms for regression and discuss their application to time series prediction tasks. We also analyze the theoretical bounds of the proposed OMKR method and conduct extensive experiments to evaluate its empirical performance on both real-world regression and times series prediction tasks.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 OnlineMultipleKernelRegression	Steven C.H. Hoi Doyen Sahoo Bin Li			Online Multiple Kernel Regression				10.1145/2623330.2623712		2014