A Decision Tree Framework for Spatiotemporal Sequence Prediction

Kim, Taehwan, Yue, Yisong, Taylor, Sarah and Matthews, Iain (2015) A Decision Tree Framework for Spatiotemporal Sequence Prediction. In: KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), AUS, pp. 577-586. ISBN 978-1-4503-3664-2

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Abstract

We study the problem of learning to predict a spatiotemporal 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 spatiotemporal 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.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 13 May 2017 05:07
Last Modified: 22 Apr 2020 11:08
URI: https://ueaeprints.uea.ac.uk/id/eprint/63506
DOI: 10.1145/2783258.2783356

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