A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
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This article is published in Annals of Mathematical Statistics.The article was published on 1970-02-01 and is currently open access. It has received 4618 citations till now. The article focuses on the topics: Examples of Markov chains & Markov chain.read more
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Partial speech processing device and method for use in distributed systems
TL;DR: In this article, a client device incorporates partial speech recognition for recognizing a spoken query by a user, and the full recognition process is distributed over a client/server architecture, so that the amount of partial recognition signal processing tasks can be allocated on a dynamic basis based on processing resources, channel conditions, etc.
Reinforcement learning for factored markov decision processes
Geoffrey E. Hinton,Brian Sallans +1 more
TL;DR: This thesis presents a thesis in which novel algorithms are presented for learning the dynamics, learning the value function, and selecting good actions for Markov decision processes, and Simulation results show that these new methods can be used to solve very large problems.
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A Hidden Markov Model approach for appearance-based 3D object recognition
TL;DR: This analysis suggests that the proposed approach represents an interesting alternative to classic appearance-based methods to 3D object classification, paying particular attention to the model selection issue and to the training procedure initialization.
DissertationDOI
On supervised learning from sequential data with applications for speech regognition
TL;DR: The use of this decoder, which can handle arbitrary order N-gram language models and arbitrary order context-dependent acoustic models with full crossword expansion, led to the best reported recognition results on the standard test set of a widely used Japanese newspaper dictation task.
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Fast training of recurrent networks based on the EM algorithm
Sheng Ma,Chuanyi Ji +1 more
TL;DR: The expectation-maximization (EM) algorithm is applied to derive a new fast training algorithm for recurrent networks through mean-field approximation, which converts training a complicated recurrent network into training an array of individual feedforward neurons.
References
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Journal ArticleDOI
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Leonard E. Baum,Ted Petrie +1 more
Journal ArticleDOI
An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
Leonard E. Baum,J. A. Eagon +1 more
TL;DR: In this paper, a polynomial with nonnegative coefficients homogeneous of degree d in its variables is shown to be polynomially homogeneous unless 3(3(x))>P(x), where 3(x)=x.
Book ChapterDOI
The Gamma Function
Willi Freeden,Martin Gutting +1 more
TL;DR: The Gamma function as discussed by the authors is a generalized factorial function that can be used to estimate the probability distribution of a probability distribution, and it has been used in many applications, e.g., as part of probability distributions.