Markovian Models for Sequential Data

Markovian Models for Sequential Data (1996)

by Yoshua Bengio

Citations:

69 - 2 self

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Datum	Value	Source
TITLE	Markovian Models for Sequential Data	SVM HeaderParse 0.1
AUTHOR NAME	Yoshua Bengio	user correction - Legacy Corrections
AUTHOR AFFIL	Dept. Informatique et Recherche Op'erationnelle	user correction - Legacy Corrections
AUTHOR ADDR	Universit'e de Montr'eal, Montreal, Qc H3C-3J7, Canada	user correction - Legacy Corrections
ABSTRACT	Hidden Markov Models (HMMs) are statistical models of sequential data that have been used successfully in many machine learning applications, especially for speech recognition. Furthermore, in the last few years, many new and promising probabilistic models related to HMMs have been proposed. We first summarize the basics of HMMs, and then review several recent related learning algorithms and extensions of HMMs, including in particular hybrids of HMMs with artificial neural networks, Input-Output HMMs (which are conditional HMMs using neural networks to compute probabilities), weighted transducers, variable-length Markov models and Markov switching state-space models. Finally, we discuss some of the challenges of future research in this very active area. 1 Introduction Hidden Markov Models (HMMs) are statistical models of sequential data that have been used successfully in many applications in artificial intelligence, pattern recognition, speech recognition, and modeling of biological ...	user correction - Legacy Corrections
YEAR	1996	user correction - Legacy Corrections
CITATIONS	141 found	ParsCit 1.0