2013 TowardsNeverEndingLearningfromT

• (Hao et al., 2013) ⇒ Yuan Hao, Yanping Chen, Jesin Zakaria, Bing Hu, Thanawin Rakthanmanon, and Eamonn Keogh. (2013). “Towards Never-ending Learning from Time Series Streams.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487634

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Towards+Never-ending+Learning+from+Time+Series+Streams
• http://dl.acm.org/citation.cfm?id=2487575.2487634&preflayout=flat#citedby

Quotes

Author Keywords

• Classification; data mining; data streams; never-ending learning; time series

Abstract

Time series classification has been an active area of research in the data mining community for over a decade, and significant progress has been made in the tractability and accuracy of learning. However, virtually all work assumes a one-time training session in which labeled examples of all the concepts to be learned are provided. This assumption may be valid in a handful of situations, but it does not hold in most medical and scientific applications where we initially may have only the vaguest understanding of what concepts can be learned. Based on this observation, we propose a never-ending learning framework for time series in which an agent examines an unbounded stream of data and occasionally asks a teacher (which may be a human or an algorithm) for a label. We demonstrate the utility of our ideas with experiments in domains as diverse as medicine, entomology, wildlife monitoring, and human behavior analyses.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 TowardsNeverEndingLearningfromT	Eamonn Keogh Thanawin Rakthanmanon Jesin Zakaria Yanping Chen Bing Hu Yuan Hao			Towards Never-ending Learning from Time Series Streams				10.1145/2487575.2487634		2013