Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Statistical methods for speech recognition
Statistical methods for speech recognition
Discrete-time, Discrete-valued Observable Operator Models: a Tutorial
Discrete-time, Discrete-valued Observable Operator Models: a Tutorial
Learning and discovery of predictive state representations in dynamical systems with reset
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning predictive state representations in dynamical systems without reset
ICML '05 Proceedings of the 22nd international conference on Machine learning
Observable Operator Models for Discrete Stochastic Time Series
Neural Computation
Learning predictive state representations using non-blind policies
ICML '06 Proceedings of the 23rd international conference on Machine learning
Predictive linear-Gaussian models of controlled stochastic dynamical systems
ICML '06 Proceedings of the 23rd international conference on Machine learning
Predictive state representations with options
ICML '06 Proceedings of the 23rd international conference on Machine learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Asymptotic Mean Stationarity of Sources With Finite Evolution Dimension
IEEE Transactions on Information Theory
Norm-observable operator models
Neural Computation
Closing the learning-planning loop with predictive state representations
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Closing the learning-planning loop with predictive state representations
International Journal of Robotics Research
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Observable operator models (OOMs) generalize hidden Markov models (HMMs) and can be represented in a structurally similar matrix formalism. The mathematical theory of OOMs gives rise to a family of constructive, fast, and asymptotically correct learning algorithms, whose statistical efficiency, however, depends crucially on the optimization of two auxiliary transformation matrices. This optimization task is nontrivial; indeed, even formulating computationally accessible optimality criteria is not easy. Here we derive how a bound on the modeling error of an OOM can be expressed in terms of these auxiliary matrices, which in turn yields an optimization procedure for them and finally affords us with a complete learning algorithm: the error-controlling algorithm. Models learned by this algorithm have an assured error bound on their parameters. The performance of this algorithm is illuminated by comparisons with two types of HMMs trained by the expectation-maximization algorithm, with the efficiency-sharpening algorithm, another recently found learning algorithm for OOMs, and with predictive state representations (Littman & Sutton, 2001) trained by methods representing the state of the art in that field.