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Prediction Suffix Tree

A Prediction Suffix Tree is a mathematical technique for modelling sequences.
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Self-Bounded Prediction Suffix Tree via Approximate String Matching Appendix for Self-Bounded Prediction Suffix Tree via Approximate String Matching A . Proof of Lemma 2
  • 2018
  • Corpus ID: 260446791
Lemma 5. Let !(i, k) = ((1 ⇠)k i exp( )/i!)1/2. Given binary sequence y 1 1 and arbitrary suffix tree T equipped with Hamming… 
2018
2018
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Prediction suffix trees (PST) provide an effective tool for sequence modelling and prediction. Current prediction techniques for… 
2007
2007
Using Approaches Based on Markovian Architectural Bias
Several authors have reported interesting results obtained by using untrained randomly initialized recurrent part of an recurrent… 
2007
2007
Efficient Mixed-Order Hidden Markov Model Inference
  • 2007
  • Corpus ID: 265757574
Higher-order Markov models are more powerful than first-order models, but suffer from an exponential increase in model parameters… 
2006
2006
Looping suffix tree-based inference of partially observable hidden state
We present a solution for inferring hidden state from sensorimotor experience when the environment takes the form of a POMDP with… 
2004
2004
On-Line Cumulative Learning of Hierarchical Sparse -grams
We present a system for on-line, cumulative learning of hierarchical collections of frequent patterns from unsegmented data… 
2003
2003
Online Prediction of Semi-structured Data Streams and Its Application to XML Data Compression
Discrete sequence prediction has a wide range of applications, e.g., sequence pattern discovery, anomaly detection, biological… 
2003
2003
Prediction suffix trees for supervised classification of sequences
2000
2000
Efficient mixed-order hidden Markov model inference
Recent studies have shown that high-order hidden Markov models (HMMs) are feasible and useful for spoken language processing… 
1996
1996
Beyond Word N-Grams
We describe, analyze, and experimentally evaluate a new probabilistic model for wordsequence prediction in natural languages… 
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