Juan Císcar, A.; Sanchis Navarro, JA.; Civera Saiz, J. (2019). Forward and Backward algorithms. http://hdl.handle.net/10251/122821
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/122821
Title:
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Forward and Backward algorithms
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Author:
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Juan Císcar, Alfonso
Sanchis Navarro, José Alberto
Civera Saiz, Jorge
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UPV Unit:
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Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
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Issued date:
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Abstract:
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The training objectives of the learning object are: 1) To explain the difficulty of computing the probability of a string with a Hidden Markov Model (HMM); 2) To compute the prob. of a string with the Forward algorithm; ...[+]
The training objectives of the learning object are: 1) To explain the difficulty of computing the probability of a string with a Hidden Markov Model (HMM); 2) To compute the prob. of a string with the Forward algorithm; and 3) To compute the prob. of a string with the Backward algorithm. In this regard, it is worth noting that, given an HMM, both the Forward and Backward algorithms are commonly used for efficient computation of the exact probability of a string. In this learning object, these algorithms are described at a basic level with the help of simple examples.
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Subjects:
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Intelligent systems
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Machine learning
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Hidden Markov models
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Languages and computer systems
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1203 - Computer Sciences
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UNESCO code:
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1203 - Ciencias de la Computación
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Copyrigths:
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Reconocimiento (by)
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Publisher:
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Universitat Politècnica de València
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Type:
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Objeto de aprendizaje
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URL:
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https://polimedia.upv.es/visor/?id=7ac7cf10-70b4-11e9-a7d3-3df1cef1857d
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Learning Resource Type:
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Polimedia
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Educational description:
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Learning object to introduce the Forward and Backward algorithms for Hidden Markov Models (HMMs). It is recommended as introductory material for the student to prepare a class in advance on the Forward and Backward algorithms for HMMs. It can also be used as a refresher on these algorithms and, in connection to this, it is advisable for the student to redo the very simple examples provided by hand. On the other hand, for the lecturer using this learning object as recommended material, it is convenient to supplement it by additional in-class exercises.
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Intended End User Role:
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Alumno
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Context:
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Primer ciclo
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Difficulty:
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Dificultad media
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Interactivity Level:
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Muy bajo
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Semantic Density:
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Medio
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Typical Learning Time:
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15 horas 00 minutos
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Educational language:
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Inglés
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Access rigths:
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PUBLICO
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