An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
An introduction to symbolic dynamics and coding
An introduction to symbolic dynamics and coding
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Introduction to Automata Theory, Languages and Computability
Introduction to Automata Theory, Languages and Computability
Symbolic dynamic analysis of complex systems for anomaly detection
Signal Processing
Blind construction of optimal nonlinear recursive predictors for discrete sequences
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Modeling word burstiness using the Dirichlet distribution
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Symbolic time series analysis via wavelet-based partitioning
Signal Processing - Special section: Distributed source coding
Symbolic Models for Nonlinear Control Systems: Alternating Approximate Bisimulations
SIAM Journal on Control and Optimization
Identification of Continuous-time Models from Sampled Data
Identification of Continuous-time Models from Sampled Data
Optimization of symbolic feature extraction for pattern classification
Signal Processing
Performance comparison of feature extraction algorithms for target detection and classification
Pattern Recognition Letters
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This paper addresses pattern classification in dynamical systems, where the underlying algorithms are formulated in the symbolic domain and the patterns are constructed from symbol strings as probabilistic finite state automata (PFSA) with (possibly) diverse algebraic structures. A combination of Dirichlet and multinomial distributions is used to model the uncertainties due to the (finite-length) string approximation of symbol sequences in both training and testing phases of pattern classification. The classifier algorithm follows the structure of a Bayes model and has been validated on a simulation test bed. The results of numerical simulation are presented for several examples.