Probabilistic finite-state machines--part I.
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Abstract
Probabilistic finite-state machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition, and machine translation are some of them. In Part I of this paper, we survey these generative objects and study their definitions and properties. In Part II, we will study the relation of probabilistic finite-state automata with other well-known devices that generate strings as hidden Markov models and n-grams and provide theorems, algorithms, and properties that represent a current state of the art of these objects.
Medical subject headings
- Algorithms
- Artificial Intelligence
- Information Storage and Retrieval
- Models, Statistical
- Natural Language Processing
- Pattern Recognition, Automated
- Signal Processing, Computer-Assisted