Probabilistic finite-state machines--part I.
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.
Vidal
Enrique
E
Departamento de Sistemas Informáticos y Computación and Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Camino de Vera s/n, E-46071 Valencia, Spain. evidal@iti.upv.es
Thollard
Franck
F
de la Higuera
Colin
C
Casacuberta
Francisco
F
Carrasco
Rafael C
RC
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D009323:Natural Language Processing; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods; D017421:Sequence Analysis / Q000379:methods; D012815:Signal Processing, Computer-Assisted