2015 ADecisionTreeFrameworkforSpatio

From GM-RKB
Jump to: navigation, search

Subject Headings:

Notes

Cited By

Quotes

Author Keywords

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

;

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