2015 ADecisionTreeFrameworkforSpatio

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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.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2015 ADecisionTreeFrameworkforSpatioTaehwan Kim
Yisong Yue
Sarah Taylor
Iain Matthews
A Decision Tree Framework for Spatiotemporal Sequence Prediction10.1145/2783258.27833562015