- (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
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.
|2015 ADecisionTreeFrameworkforSpatio||Taehwan Kim|
|A Decision Tree Framework for Spatiotemporal Sequence Prediction||10.1145/2783258.2783356||2015|
|Author||Taehwan Kim +, Yisong Yue +, Sarah Taylor + and Iain Matthews +|
|proceedings||Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||A Decision Tree Framework for Spatiotemporal Sequence Prediction +|