A Decision Tree Framework for Spatiotemporal Sequence Prediction
A Decision Tree Framework for Spatiotemporal Sequence Prediction
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DOI：
10.1145/2783258.2783356
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发表时间：
2015-08
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影响因子：
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通讯作者：
Taehwan Kim;Yisong Yue;Sarah L. Taylor;I. Matthews
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文献类型：
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作者：
Taehwan Kim;Yisong Yue;Sarah L. Taylor;I. Matthews
我们研究了在给定输入序列的时期学习的问题，以预测时空输出序列。与常规序列预测问题（例如，使用相对较小的离散标签集选择输出序列）相反，我们的目标是预测位于高维连续输出空间内的序列。我们提出了一个决策树框架，用于学习准确的非参数时空序列预测指标。我们的方法享有几种有吸引力的特性，包括易于培训，测试时间快速性能以及使用新型潜在变量方法可耐受损坏的训练数据的能力。我们在几个数据集上进行了评估，并证明了对基于决策树的序列学习框架（例如Searn和Dagger）的实质性改进。
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.