Oral
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Dongwoo Kim · Christian Walder

Thu Jul 12th 05:30 -- 05:40 PM @ A5

Prediction suffix trees (PST) provide an effective tool for sequence modelling and prediction. Current prediction techniques for PSTs rely on exact matching between the suffix of the current sequence and the previously observed sequence. We present a provably correct algorithm for learning a PST with approximate suffix matching by relaxing the exact matching condition. We then present a self-bounded enhancement of our algorithm where the depth of suffix tree grows automatically in response to the model performance on a training sequence. Through experiments on synthetic datasets as well as three real-world datasets, we show that the approximate matching PST results in better predictive performance than the other variants of PST.

Author Information

Dongwoo Kim (The Australian National University)
Christian Walder (Data61, the Australian National University)

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