2015 ADecisionTreeFrameworkforSpatio

• (Kim et al., 2015) ⇒ Taehwan Kim, Yisong Yue, Sarah Taylor, and Iain Matthews. (2015). “A Decision Tree Framework for Spatiotemporal Sequence Prediction.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783356

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+A+Decision+Tree+Framework+for+Spatiotemporal+Sequence+Prediction
• http://dl.acm.org/citation.cfm?id=2783258.2783356&preflayout=flat#citedby

Quotes

Author Keywords

• Decision trees; induction; sequence prediction

Abstract

We study the problem of learning to predict a spatio-temporal output sequence given an input sequence. In contrast to conventional sequence prediction problems such as part-of-speech tagging (where output sequences are selected using a relatively small set of discrete labels), our goal is to predict sequences that lie within a high-dimensional continuous output space. We present a decision tree framework for learning an accurate non-parametric spatio-temporal sequence predictor. Our approach enjoys several attractive properties, including ease of training, fast performance at test time, and the ability to robustly tolerate corrupted training data using a novel latent variable approach. We evaluate on several datasets, and demonstrate substantial improvements over existing decision tree based sequence learning frameworks such as SEARN and DAgger.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 ADecisionTreeFrameworkforSpatio	Yisong Yue Iain Matthews Taehwan Kim Sarah Taylor			A Decision Tree Framework for Spatiotemporal Sequence Prediction				10.1145/2783258.2783356		2015