Joint semi-supervised learning of Hidden Conditional Random Fields and Hidden Markov Models

2014 ◽  
Vol 37 ◽  
pp. 161-171 ◽  
Author(s):  
Yann Soullard ◽  
Martin Saveski ◽  
Thierry Artières
Keyword(s):  
Supervised Learning ◽  
Hidden Markov Models ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Markov Models ◽  
Hidden Markov

AUV behavior recognition using behavior histograms, HMMs, and CRFs

Robotica ◽  
2014 ◽  
Vol 32 (2) ◽  
pp. 291-304 ◽  
Author(s):  
Michael Novitzky ◽  
Charles Pippin ◽  
Thomas R. Collins ◽  
Tucker R. Balch ◽  
Michael E. West
Keyword(s):  
Hidden Markov Models ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Markov Models ◽  
Hidden Markov ◽  
Task Assignment ◽  
Behavior Recognition ◽  
Sensor Information ◽  
Recognition Technique ◽  
And Task

SUMMARYThis paper focuses on behavior recognition in an underwater application as a substitute for communicating through acoustic transmissions, which can be unreliable. The importance of this work is that sensor information regarding other agents can be leveraged to perform behavior recognition, which is activity recognition of robots performing specific programmed behaviors, and task-assignment. This work illustrates the use of Behavior Histograms, Hidden Markov Models (HMMs), and Conditional Random Fields (CRFs) to perform behavior recognition. We present challenges associated with using each behavior recognition technique along with results on individually selected test trajectories, from simulated and real sonar data, and real-time recognition through a simulated mission.

Semi-supervised learning of Hidden Markov Models for biological sequence analysis

2018 ◽  
Vol 35 (13) ◽  
pp. 2208-2215 ◽  
Author(s):  
Ioannis A Tamposis ◽  
Konstantinos D Tsirigos ◽  
Margarita C Theodoropoulou ◽  
Panagiota I Kontou ◽  
Pantelis G Bagos
Keyword(s):  
Sequence Analysis ◽  
Supervised Learning ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Transmembrane Protein ◽  
Training Data ◽  
Supplementary Information ◽  
Training Procedure ◽  
Partially Labeled Data

Abstract Motivation Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications. Results We propose here, a method for semi-supervised learning of HMMs that can incorporate labeled, unlabeled and partially labeled data in a straightforward manner. The algorithm is based on a variant of the Expectation-Maximization (EM) algorithm, where the missing labels of the unlabeled or partially labeled data are considered as the missing data. We apply the algorithm to several biological problems, namely, for the prediction of transmembrane protein topology for alpha-helical and beta-barrel membrane proteins and for the prediction of archaeal signal peptides. The results are very promising, since the algorithms presented here can significantly improve the prediction performance of even the top-scoring classifiers. Supplementary information Supplementary data are available at Bioinformatics online.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document