Probabilistic finite-state machines - part I
| Autor: | Enrique Vidal, Franck Thollard, C. de la Higuera, Rafael C. Carrasco, Francisco Casacuberta |
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| Přispěvatelé: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos, Transducens |
| Rok vydání: | 2005 |
| Předmět: |
Computer science
Language models Information Storage and Retrieval computer.software_genre Syntactic pattern recognition Pattern Recognition Automated Classes defined by grammars or automata Feature (machine learning) Cluster Analysis Structural pattern recognition Hidden Markov model Parsing Applied Mathematics Signal Processing Computer-Assisted Language acquisition Computational Theory and Mathematics Pattern recognition (psychology) Probability distribution Computer Vision and Pattern Recognition Computational linguistics Sequence Analysis Algorithms Natural language Machine translation Speech recognition and synthesis Automata Text mining Artificial Intelligence Machine learning Computer Simulation Probabilistic analysis of algorithms Natural Language Processing Models Statistical Finite-state machine Learning automata business.industry Probabilistic logic Numerical Analysis Computer-Assisted Language parsing and understanding Lenguajes y Sistemas Informáticos Probabilistic automaton Artificial intelligence Language model business Sequence Alignment computer Software Generative grammar |
| Zdroj: | RUA. Repositorio Institucional de la Universidad de Alicante Universidad de Alicante (UA) |
| ISSN: | 0162-8828 |
| DOI: | 10.1109/tpami.2005.147 |
| Popis: | 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. This work has been partially supported by the Spanish project TIC2003-08681-C02 and the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. |
| Databáze: | OpenAIRE |
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