Combining a hybrid Approach for Features Selection and Hidden Markov Models in Multifont Arabic Characters Recognition

We describe a hybrid modeling approach where the parameters of a model are calculated and modulated by another model, typically a neural network (NN), to avoid both overfitting and underfitting. We develop the approach for the case of Hidden Markov Models (HMMs), by deriving a class of hybrid HMM/NN architectures. These architectures can be trained with unified algorithms that blend HMM dynamic programming with NN backpropagation. In the case of complex data, mixtures of HMMs or modulated HMMs must be used. NNs can then be applied both to the parameters of each single HMM, and to the switching or modulation of the models, as a function of input or context. Hybrid HMM/NN architectures provide a flexible NN parameterization for the control of model structure and complexity. At the same time, they can capture distributions that, in practice, are inaccessible to single HMMs. The HMM/NN hybrid approach is tested, in its simplest form, by constructing a model of the immunoglobulin protein family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs.

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Estimating Personality Impression from Speech Record Using Hidden Markov Models

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.135.1517 ◽

2015 ◽

Vol 135 (12) ◽

pp. 1517-1523 ◽

Cited By ~ 1

Author(s):

Yicheng Jin ◽

Takuto Sakuma ◽

Shohei Kato ◽

Tsutomu Kunitachi

Keyword(s):

Hidden Markov Models ◽

Markov Models ◽

Hidden Markov

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Hidden Markov Processes

10.23943/princeton/9780691133157.001.0001 ◽

2014 ◽

Cited By ~ 2

Author(s):

M. Vidyasagar

Keyword(s):

Hidden Markov Models ◽

Markov Processes ◽

Viterbi Algorithm ◽

Markov Models ◽

Hidden Markov ◽

Local Alignment ◽

Biological Applications ◽

Standard Material ◽

Hidden Markov Processes ◽

Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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