Kim, Taehwan and Taylor, Sarah and Yue, Yisong and Matthews, Iain (2015) A Decision Tree Framework for Spatiotemporal Sequence Prediction. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery , New York, NY, pp. 577-586. ISBN 978-1-4503-3664-2. https://resolver.caltech.edu/CaltechAUTHORS:20150928-132143651
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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 | |||||||||
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Additional Information: | © 2015 ACM. | |||||||||
Subject Keywords: | Decision Trees; Sequence Prediction | |||||||||
DOI: | 10.1145/2783258.2783356 | |||||||||
Record Number: | CaltechAUTHORS:20150928-132143651 | |||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20150928-132143651 | |||||||||
Official Citation: | Taehwan Kim, Yisong Yue, Sarah Taylor, and Iain Matthews. 2015. A Decision Tree Framework for Spatiotemporal Sequence Prediction. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '15). ACM, New York, NY, USA, 577-586. DOI=10.1145/2783258.2783356 http://doi.acm.org/10.1145/2783258.2783356 | |||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
ID Code: | 60579 | |||||||||
Collection: | CaltechAUTHORS | |||||||||
Deposited By: | Tony Diaz | |||||||||
Deposited On: | 29 Sep 2015 01:53 | |||||||||
Last Modified: | 10 Nov 2021 22:35 |
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