Relevant bibliographies by topics / Algorithme de Baum-Welch

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Academic literature on the topic 'Algorithme de Baum-Welch'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Algorithme de Baum-Welch"

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Assunção, Joaquim, Paulo Fernandes, and Jean-Marc Vincent. "Piecewise Aggregation for HMM Fitting: A Pre-Fitting Model for Seamless Integration with Time-Series Data." International Journal of Software Engineering and Knowledge Engineering 29, no. 11n12 (2019): 1835–50. http://dx.doi.org/10.1142/s0218194019400242.

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We propose a simple, fast, deterministic pre-fitting approach which derives the Baum–Welch algorithm initial values directly from the input data. Such pre-fitting has the purpose of improving the fitting time for a given Hidden Markov Model (HMM) while maintaining the original Baum–Welch algorithm as the fitting one. The fitting time is improved by avoiding the Baum–Welch algorithm sensitiveness through the generation of parameters closer to the global maximum likelihood. Furthermore, by keeping the original Baum–Welch algorithm as the fitting one, we guarantee that all related methods will continue to work properly. On the other hand, the pre-fitting generates the HMM parameters directly derived from time-series data, without any data transformation, using an [Formula: see text] operation.
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GOLOD, DANIIL, and DANIEL G. BROWN. "A TUTORIAL OF TECHNIQUES FOR IMPROVING STANDARD HIDDEN MARKOV MODEL ALGORITHMS." Journal of Bioinformatics and Computational Biology 07, no. 04 (2009): 737–54. http://dx.doi.org/10.1142/s0219720009004242.

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In this tutorial, we discuss two main algorithms for Hidden Markov Models or HMMs: the Viterbi algorithm and the expectation phase of the Baum–Welch algorithm, and we describe ways to improve their naïve implementations. For the Baum–Welch algorithm we first present an implementation of the expectation computations using constant space. We then discuss the classical implementation of this calculation and describe ways to reduce its space usage to logarithmic and [Formula: see text], with their respective CPU costs. We also note where each respective algorithm can be parallelized. For the Viterbi algorithm, we describe [Formula: see text] and logarithmic space algorithms which increase CPU use by a factor of two and by a logarithmic factor respectively. We also present two recent heuristics for decreasing space use, which in practice lead to logarithmic space use. Classical version of Viterbi cannot be parallelized by splitting sequence in several subsequences, but we show a parallelization that works if we are willing to pay a significant extra CPU cost. Finally we show a very simple parallelization trick which enables full usage of multiple CPUs/cores under the condition that they share memory.
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Turin, W. "Unidirectional and parallel Baum-Welch algorithms." IEEE Transactions on Speech and Audio Processing 6, no. 6 (1998): 516–23. http://dx.doi.org/10.1109/89.725318.

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Zhang, Jingwen, Fanggang Wang, Zhangdui Zhong, and Shilian Wang. "Continuous Phase Modulation Classification via Baum-Welch Algorithm." IEEE Communications Letters 22, no. 7 (2018): 1390–93. http://dx.doi.org/10.1109/lcomm.2018.2821171.

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Steeneck, Daniel, and Fredrik Eng-Larsson. "The Baum–Welch algorithm with limiting distribution constraints." Operations Research Letters 46, no. 6 (2018): 563–67. http://dx.doi.org/10.1016/j.orl.2018.08.008.

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Xu, Hui Hong, and Su Chun Gao. "Speaker Recognition Study Based on Optimized Baum-Welch Algorithm." Applied Mechanics and Materials 543-547 (March 2014): 2471–73. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2471.

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The speaker recognition is a sort of biological recognition technology according to person's sound to identify .The article based on vc platform implement speaker recognitions function using VQ and HMM technology. using genetic algorithm to improve the Baum-Welch algorithm.Trough experiment verificate that improved-arithmetic enhance recognition effect.
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Baldi, Pierre, and Yves Chauvin. "Smooth On-Line Learning Algorithms for Hidden Markov Models." Neural Computation 6, no. 2 (1994): 307–18. http://dx.doi.org/10.1162/neco.1994.6.2.307.

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A simple learning algorithm for Hidden Markov Models (HMMs) is presented together with a number of variations. Unlike other classical algorithms such as the Baum-Welch algorithm, the algorithms described are smooth and can be used on-line (after each example presentation) or in batch mode, with or without the usual Viterbi most likely path approximation. The algorithms have simple expressions that result from using a normalized-exponential representation for the HMM parameters. All the algorithms presented are proved to be exact or approximate gradient optimization algorithms with respect to likelihood, log-likelihood, or cross-entropy functions, and as such are usually convergent. These algorithms can also be casted in the more general EM (Expectation-Maximization) framework where they can be viewed as exact or approximate GEM (Generalized Expectation-Maximization) algorithms. The mathematical properties of the algorithms are derived in the appendix.
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Holmes, Ian. "Using evolutionary Expectation Maximization to estimate indel rates." Bioinformatics 21, no. 10 (2005): 2294–300. http://dx.doi.org/10.1093/bioinformatics/bti177.

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Abstract Motivation The Expectation Maximization (EM) algorithm, in the form of the Baum–Welch algorithm (for hidden Markov models) or the Inside-Outside algorithm (for stochastic context-free grammars), is a powerful way to estimate the parameters of stochastic grammars for biological sequence analysis. To use this algorithm for multiple-sequence evolutionary modelling, it would be useful to apply the EM algorithm to estimate not only the probability parameters of the stochastic grammar, but also the instantaneous mutation rates of the underlying evolutionary model (to facilitate the development of stochastic grammars based on phylogenetic trees, also known as Statistical Alignment). Recently, we showed how to do this for the point substitution component of the evolutionary process; here, we extend these results to the indel process. Results We present an algorithm for maximum-likelihood estimation of insertion and deletion rates from multiple sequence alignments, using EM, under the single-residue indel model owing to Thorne, Kishino and Felsenstein (the ‘TKF91’ model). The algorithm converges extremely rapidly, gives accurate results on simulated data that are an improvement over parsimonious estimates (which are shown to underestimate the true indel rate), and gives plausible results on experimental data (coronavirus envelope domains). Owing to the algorithm's close similarity to the Baum–Welch algorithm for training hidden Markov models, it can be used in an ‘unsupervised’ fashion to estimate rates for unaligned sequences, or estimate several sets of rates for sequences with heterogenous rates. Availability Software implementing the algorithm and the benchmark is available under GPL from http://www.biowiki.org/ Contact ihh@berkeley.edu
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Zhang, Yanxue, Dongmei Zhao, and Jinxing Liu. "The Application of Baum-Welch Algorithm in Multistep Attack." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/374260.

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The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.
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Jensen, Jens Ledet. "A Note on the Linear Memory Baum-Welch Algorithm." Journal of Computational Biology 16, no. 9 (2009): 1209–10. http://dx.doi.org/10.1089/cmb.2008.0178.

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Dissertations / Theses on the topic "Algorithme de Baum-Welch"

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Kriouile, Abdelaziz. "La reconnaissance automatique de la parole et les modèles markoviens cachés : modèles du second ordre et distance de Viterbi à optimalité locale." Nancy 1, 1990. http://www.theses.fr/1990NAN10273.

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Des travaux intensifs sur la reconnaissance automatique de la parole utilisant les modèles stochastiques ont été réalisés durant les cinq dernières années. L'application des modèles markoviens cachés (HMM) du premier ordre a conduit 0 des résultats impressionnants dans le domaine de la reconnaissance de mots isolés et de la parole continue. Notre objectif était de montrer que l'apport des modèles markoviens cachés à la reconnaissance automatique de la parole est d'autant plus important qu'on mène des réflexions fondamentales sur les modèles markoviens eux-mêmes et sur la façon de les appliquer. Nous avons développé une nouvelle formulation de Baum-Welch et une extension de l'algorithme de Viterbi, qui rendent les modèles markoviens cachés du second ordre efficaces en calcul pour des applications en temps réel. Il y avait une nette amélioration du taux de reconnaissance avec le second ordre. L'extension a des HMM d'ordre plus élevé a été aussi discutée. Enfin, nous avons proposé une nouvelle stratégie d'utilisation de l'algorithme de Viterbi pour la reconnaissance de la parole continue. Elle est basée sur la comparaison d'optimums locaux dans une fenêtre de trames. Cette stratégie, par bloc, a donné de meilleurs résultats que les versions classiques de l'algorithme de Viterbi. Elle permet une interaction avec d'autres processeurs.
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Puengnim, Anchalee. "Classification de modulations linéaires et non-linéaires à l'aide de méthodes bayésiennes." Toulouse, INPT, 2008. http://ethesis.inp-toulouse.fr/archive/00000676/.

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La reconnaissance de modulations numériques consiste à identifier, au niveau du récepteur d'une chaîne de transmission, l'alphabet auquel appartiennent les symboles du message transmis. Cette reconnaissance est nécessaire dans de nombreux scénarios de communication, afin, par exemple, de sécuriser les transmissions pour détecter d'éventuels utilisateurs non autorisés ou bien encore de déterminer quel terminal brouille les autres. Le signal observé en réception est généralement affecté d'un certain nombre d'imperfections, dues à une synchronisation imparfaite de l'émetteur et du récepteur, une démodulation imparfaite, une égalisation imparfaite du canal de transmission. Nous proposons plusieurs méthodes de classification qui permettent d'annuler les effets liés aux imperfections de la chaîne de transmission. Les symboles reçus sont alors corrigés puis comparés à ceux du dictionnaire des symboles transmis<br>This thesis studies classification of digital linear and nonlinear modulations using Bayesian methods. Modulation recognition consists of identifying, at the receiver, the type of modulation signals used by the transmitter. It is important in many communication scenarios, for example, to secure transmissions by detecting unauthorized users, or to determine which transmitter interferes the others. The received signal is generally affected by a number of impairments. We propose several classification methods that can mitigate the effects related to imperfections in transmission channels. More specifically, we study three techniques to estimate the posterior probabilities of the received signals conditionally to each modulation
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Majewsky, Stefan. "Training of Hidden Markov models as an instance of the expectation maximization algorithm." Bachelor's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-226903.

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In Natural Language Processing (NLP), speech and text are parsed and generated with language models and parser models, and translated with translation models. Each model contains a set of numerical parameters which are found by applying a suitable training algorithm to a set of training data. Many such training algorithms are instances of the Expectation-Maximization (EM) algorithm. In [BSV15], a generic EM algorithm for NLP is described. This work presents a particular speech model, the Hidden Markov model, and its standard training algorithm, the Baum-Welch algorithm. It is then shown that the Baum-Welch algorithm is an instance of the generic EM algorithm introduced by [BSV15], from which follows that all statements about the generic EM algorithm also apply to the Baum-Welch algorithm, especially its correctness and convergence properties.
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Danielsson, Simon, and Jakob Flygare. "A Multi-Target Graph-Constrained HMM Localisation Approach using Sparse Wi-Fi Sensor Data." Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231090.

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This thesis explored the possibilities of using a Hidden Markov Model approach for multi-target localisation in an urban environment, with observations generated from Wi-Fi sensors. The area is modelled as a network of nodes and arcs, where the arcs represent sidewalks in the area and constitutes the hidden states in the model. The output of the model is the expected amount of people at each road segment throughout the day. In addition to this, two methods for analyzing the impact of events in the area are proposed. The first method is based on a time series analysis, and the second one is based on the updated transition matrix using the Baum-Welch algorithm. Both methods reveal which road segments are most heavily affected by a surge of traffic in the area, as well as potential bottleneck areas where congestion is likely to have occurred.<br>I det här examensarbetet har lokalisering av gångtrafikanter med hjälp av Hidden Markov Models utförts. Lokaliseringen är byggd på data från Wi-Fi sensorer i ett område i Stockholm. Området är modellerat som ett graf-baserat nätverk där linjerna mellan noderna representerar möjliga vägar för en person att befinna sig på. Resultatet för varje individ är aggregerat för att visa förväntat antal personer på varje segment över en hel dag. Två metoder för att analysera hur event påverkar området introduceras och beskrivs. Den första är baserad på tidsserieanalys och den andra är en maskinlärningsmetod som bygger på Baum-Welch algoritmen. Båda metoderna visar vilka segment som drabbas mest av en snabb ökning av trafik i området och var trängsel är troligt att förekomma.
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Khan, Anwer Ali. "Iterative Decoding and Channel Estimation over Hidden Markov Fading Channels." Thesis, Virginia Tech, 2000. http://hdl.handle.net/10919/32470.

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<p>Since the 1950s, hidden Markov models (HMMS) have seen widespread use in electrical engineering. Foremost has been their use in speech processing, pattern recognition, artificial intelligence, queuing theory, and communications theory. However, recent years have witnessed a renaissance in the application of HMMs to the analysis and simulation of digital communication systems. Typical applications have included signal estimation, frequency tracking, equalization, burst error characterization, and transmit power control. Of special significance to this thesis, however, has been the use of HMMs to model fading channels typical of wireless communications. This variegated use of HMMs is fueled by their ability to model time-varying systems with memory, their ability to yield closed form solutions to otherwise intractable analytic problems, and their ability to help facilitate simple hardware and/or software based implementations of simulation test-beds. </p> <p> The aim of this thesis is to employ and exploit hidden Markov fading models within an iterative (turbo) decoding framework. Of particular importance is the problem of channel estimation, which is vital for realizing the large coding gains inherent in turbo coded schemes. This thesis shows that a Markov fading channel (MFC) can be conceptualized as a trellis, and that the transmission of a sequence over a MFC can be viewed as a trellis encoding process much like convolutional encoding. The thesis demonstrates that either maximum likelihood sequence estimation (MLSE) algorithms or maximum <I> a posteriori</I> (MAP) algorithms operating over the trellis defined by the MFC can be used for channel estimation. Furthermore, the thesis illustrates sequential and decision-directed techniques for using the aforementioned trellis based channel estimators <I>en masse</I> with an iterative decoder.</p><br>Master of Science
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Wilhelmsson, Anna, and Sofia Bedoire. "Driving Behavior Prediction by Training a Hidden Markov Model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291656.

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Introducing automated vehicles in to traffic withhuman drivers, human behavior prediction is essential to obtainoperation safety. In this study, a human behavior estimationmodel has been developed. The estimations are based on aHidden Markov Model (HMM) using observations to determinethe driving style of surrounding vehicles. The model is trainedusing two different methods: Baum Welch training and Viterbitraining to improve the performance. Both training methods areevaluated by looking at time complexity and convergence. Themodel is implemented with and without training and tested fordifferent driving styles. Results show that training is essentialfor accurate human behavior prediction. Viterbi training is fasterbut more noise sensitive compared to Baum Welch training. Also,Viterbi training produces good results if training data reflects oncurrently observed driver, which is not always the case. BaumWelch training is more robust in such situations. Lastly, BaumWelch training is recommended to obtain operation safety whenintroducing automated vehicles into traffic.<br>N ̈ar automatiserade fordon introduceras itrafiken och beh ̈over interagera med m ̈anskliga f ̈orare ̈ar det vik-tigt att kunna f ̈orutsp ̊a m ̈anskligt beteende. Detta f ̈or att kunnaerh ̊alla en s ̈akrare trafiksituation. I denna studie har en modellsom estimerar m ̈anskligt beteende utvecklats. Estimeringarna ̈ar baserade p ̊a en Hidden Markov Model d ̈ar observationeranv ̈ands f ̈or att best ̈amma k ̈orstil hos omgivande fordon itrafiken. Modellen tr ̈anas med tv ̊a olika metoder: Baum Welchtr ̈aning och Viterbi tr ̈aning f ̈or att f ̈orb ̈attra modellens prestanda.Tr ̈aningsmetoderna utv ̈arderas sedan genom att analysera derastidskomplexitet och konvergens. Modellen ̈ar implementerad medoch utan tr ̈aning och testad f ̈or olika k ̈orstilar. Erh ̊allna resultatvisar att tr ̈aning ̈ar viktigt f ̈or att kunna f ̈orutsp ̊a m ̈anskligtbeteende korrekt. Viterbi tr ̈aning ̈ar snabbare men mer k ̈ansligf ̈or brus i j ̈amf ̈orelse med Baum Welch tr ̈aning. Viterbi tr ̈aningger ̈aven en bra estimering i de fall d ̊a observerad tr ̈aningsdataavspeglar f ̈orarens k ̈orstil, vilket inte alltid ̈ar fallet. BaumWelch tr ̈aning ̈ar mer robust i s ̊adana situationer. Slutligenrekommenderas en estimeringsmodell implementerad med BaumWelch tr ̈aning f ̈or att erh ̊alla en s ̈aker k ̈orning d ̊a automatiseradefordon introduceras i trafiken<br>Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
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Wåhlin, Peter. "Enhanching the Human-Team Awareness of a Robot." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-16371.

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The use of autonomous robots in our society is increasing every day and a robot is no longer seen as a tool but as a team member. The robots are now working side by side with us and provide assistance during dangerous operations where humans otherwise are at risk. This development has in turn increased the need of robots with more human-awareness. Therefore, this master thesis aims at contributing to the enhancement of human-aware robotics. Specifically, we are investigating the possibilities of equipping autonomous robots with the capability of assessing and detecting activities in human teams. This capability could, for instance, be used in the robot's reasoning and planning components to create better plans that ultimately would result in improved human-robot teamwork performance. we propose to improve existing teamwork activity recognizers by adding intangible features, such as stress, motivation and focus, originating from human behavior models. Hidden markov models have earlier been proven very efficient for activity recognition and have therefore been utilized in this work as a method for classification of behaviors. In order for a robot to provide effective assistance to a human team it must not only consider spatio-temporal parameters for team members but also the psychological.To assess psychological parameters this master thesis suggests to use the body signals of team members. Body signals such as heart rate and skin conductance. Combined with the body signals we investigate the possibility of using System Dynamics models to interpret the current psychological states of the human team members, thus enhancing the human-awareness of a robot.<br>Användningen av autonoma robotar i vårt samhälle ökar varje dag och en robot ses inte längre som ett verktyg utan som en gruppmedlem. Robotarna arbetar nu sida vid sida med oss och ger oss stöd under farliga arbeten där människor annars är utsatta för risker. Denna utveckling har i sin tur ökat behovet av robotar med mer människo-medvetenhet. Därför är målet med detta examensarbete att bidra till en stärkt människo-medvetenhet hos robotar. Specifikt undersöker vi möjligheterna att utrusta autonoma robotar med förmågan att bedöma och upptäcka olika beteenden hos mänskliga lag. Denna förmåga skulle till exempel kunna användas i robotens resonemang och planering för att ta beslut och i sin tur förbättra samarbetet mellan människa och robot. Vi föreslår att förbättra befintliga aktivitetsidentifierare genom att tillföra förmågan att tolka immateriella beteenden hos människan, såsom stress, motivation och fokus. Att kunna urskilja lagaktiviteter inom ett mänskligt lag är grundläggande för en robot som ska vara till stöd för laget. Dolda markovmodeller har tidigare visat sig vara mycket effektiva för just aktivitetsidentifiering och har därför använts i detta arbete. För att en robot ska kunna ha möjlighet att ge ett effektivt stöd till ett mänskligtlag måste den inte bara ta hänsyn till rumsliga parametrar hos lagmedlemmarna utan även de psykologiska. För att tyda psykologiska parametrar hos människor förespråkar denna masteravhandling utnyttjandet av mänskliga kroppssignaler. Signaler så som hjärtfrekvens och hudkonduktans. Kombinerat med kroppenssignalerar påvisar vi möjligheten att använda systemdynamiksmodeller för att tolka immateriella beteenden, vilket i sin tur kan stärka människo-medvetenheten hos en robot.<br><p>The thesis work was conducted in Stockholm, Kista at the department of Informatics and Aero System at Swedish Defence Research Agency.</p>
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Bakri, Satria Arief Wicaksono, and Satria Arief Wicaksono Bakri. "Component Failure Pattern Recognition in Notebook Computer by using Hidden Markov Model based on Adaptive Cellular Genetic-Baum Welch Algorithm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/m6ubzg.

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碩士<br>國立臺灣科技大學<br>工業管理系<br>106<br>For notebook computer companies, managing component inventory for repair service centers is vital. While there are many works performed forecast in repair time and repair volume of components, there is a limited number of research performs the pattern recognition for component failure in notebook computers. This work, in the quest of providing valuable inputs for the inventory management practice of repair service center, will focus recognizing the pattern of component failure in notebook computers. Sequential repair history was gathered from a notebook computer repair service center in Taiwan and treated as sets of observations sequences of a hidden Markov model (HMM). Meanwhile, the component failure is treated as the hidden states. The pre-processing of raw data is carried out and revealed an imbalanced HMM structure. To tackle this, a cellular Genetic Algorithm (cGA) with dominance chromosome mechanism is proposed to train the HMM. Furthermore, to enhance the performance of the proposed algorithm, an adaptive feature to switch the dominance chromosome ratio and a feature to re-estimate the fitness value using Baum-Welch Algorithm is proposed. This proposed algorithm is then called Adaptive cGA-BW and, subsequently trained the HMM for 2099 observation sequence instances. A comparative study among conventional algorithm to train the HMM and other variants of cGA is employed. This study shows Adaptive cGA-BW performed significantly better than Baum-Welch Algorithm. This result is verified by Kruskal-Wallis test. To understand the most probable component failure pattern, Viterbi Algorithm based on the HMM trained by Adaptive cGA-BW is implemented. The algorithm decoded the 70% most occurring observation sequences to component failure patterns. These patterns are ranked by their probability of happening.
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(5930648), Suhas Gundimeda. "Statistical inference of time-dependent data." Thesis, 2020.

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Probabilistic graphical modeling is a framework which can be used to succinctly<br>represent multivariate probability distributions of time series in terms of each time<br>series’s dependence on others. In general, it is computationally prohibitive to sta-<br>tistically infer an arbitrary model from data. However, if we constrain the model to<br>have a tree topology, the corresponding learning algorithms become tractable. The<br>expressive power of tree-structured distributions are low, since only n − 1 dependen-<br>cies are explicitly encoded for an n node tree. One way to improve the expressive<br>power of tree models is to combine many of them in a mixture model. This work<br>presents and uses simulations to validate extensions of the standard mixtures of trees<br>model for i.i.d data to the setting of time series data. We also consider the setting<br>where the tree mixture itself forms a hidden Markov chain, which could be better<br>suited for approximating time-varying seasonal data in the real world. Both of these<br>are evaluated on artificial data sets.<br><br>
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Book chapters on the topic "Algorithme de Baum-Welch"

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Munro, Paul, Hannu Toivonen, Geoffrey I. Webb, et al. "Baum-Welch Algorithm." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_59.

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Koski, Timo. "Baum-Welch Learning Algorithm." In Hidden Markov Models for Bioinformatics. Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-010-0612-5_15.

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Schmidt, Kim, and Karl Heinz Hoffmann. "Modified Baum Welch Algorithm for Hidden Markov Models with Known Structure." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11051-2_75.

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Benyacoub, Badreddine, Ismail ElMoudden, Souad ElBernoussi, Abdelhak Zoglat, and Mohamed Ouzineb. "Initial Model Selection for the Baum-Welch Algorithm Applied to Credit Scoring." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18167-7_31.

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Rodríguez, Luis Javier, and Inés Torres. "Comparative Study of the Baum-Welch and Viterbi Training Algorithms Applied to Read and Spontaneous Speech Recognition." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_98.

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"Baum-Welch Algorithm." In Encyclopedia of Biometrics. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_539.

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"Baum-Welch Algorithm." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_59.

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Vidyasagar, M. "Hidden Markov Processes: Basic Properties." In Hidden Markov Processes. Princeton University Press, 2014. http://dx.doi.org/10.23943/princeton/9780691133157.003.0006.

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This chapter considers the basic properties of hidden Markov processes (HMPs) or hidden Markov models (HMMs), a special type of stochastic process. It begins with a discussion of three distinct types of HMMs and shows that they are all equivalent from the standpoint of their expressive power or modeling ability: Type 1 hidden Markov model, or a HMM of the deterministic function of a Markov chain type; hidden Markov model of Type 2, or a HMM of the random function of a Markov chain type; and hidden Markov model of Type 3, or a HMM of the joint Markov process type. The chapter also examines various issues related to the computation of likelihoods in a HMM before concluding with an overview of the Viterbi algorithm and the Baum–Welch algorithm.
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Conference papers on the topic "Algorithme de Baum-Welch"

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El Gemayel, Noha, Javier Schloemann, R. Michael Buehrer, and Friedrich K. Jondral. "Improved Indoor Positioning Using the Baum-Welch Algorithm." In 2015 IEEE Globecom Workshops (GC Wkshps). IEEE, 2015. http://dx.doi.org/10.1109/glocomw.2015.7414025.

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Yang, Fanny, Sivaraman Balakrishnan, and Martin J. Wainwright. "Statistical and computational guarantees for the Baum-Welch algorithm." In 2015 53rd Annual Allerton Conference on Communication, Control and Computing (Allerton). IEEE, 2015. http://dx.doi.org/10.1109/allerton.2015.7447067.

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Oudelha, Mourad, and Raja N. Ainon. "HMM parameters estimation using hybrid Baum-Welch genetic algorithm." In 2010 International Symposium on Information Technology (ITSim 2010). IEEE, 2010. http://dx.doi.org/10.1109/itsim.2010.5561388.

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Nootyaskool, Supakit, and Boontee Kruatrachue. "Hybrid Genetic Algorithm with Baum-Welch Algorithm by using Diversity Population Technique." In 2006 International Symposium on Communications and Information Technologies. IEEE, 2006. http://dx.doi.org/10.1109/iscit.2006.339879.

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Huggins-Daines, David, and Alexander I. Rudnicky. "A constrained baum-welch algorithm for improved phoneme segmentation and efficient training." In Interspeech 2006. ISCA, 2006. http://dx.doi.org/10.21437/interspeech.2006-364.

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Patel, Pushpraj, and Vijay Prakash. "FTAB: Fault tolerance approach by using HMM with BAUM-WELCH algorithm in MCC." In 2013 Tenth International Conference on Wireless and Optical Communications Networks - (WOCN). IEEE, 2013. http://dx.doi.org/10.1109/wocn.2013.6616256.

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Zhang, Liang-li, Chao-zhong Wu, Zhen Huang, and Bin Wang. "Optimizing Hidden Markov Model with Baum-Welch Algorithm for Vehicle Driver's Intention Recognition." In Second International Conference on Transportation Information and Safety. American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784413036.181.

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Anandhalekshmi, A. V., V. Srinivasa Rao, and G. R. Kanagachidambaresan. "HMM Based on Baum-Welch Algorithm for Predicting Critical Data Packets in IoT Network." In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2020. http://dx.doi.org/10.1109/icccnt49239.2020.9225343.

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Hsiao, Roger, Yik-Cheung Tam, and Tanja Schultz. "Generalized Baum-Welch algorithm for discriminative training on large vocabulary continuous speech recognition system." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960447.

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Zhang, Xueying, Yiping Wang, and Zhefeng Zhao. "A Hybrid Speech Recognition Training Method for HMM Based on Genetic Algorithm and Baum Welch Algorithm." In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icicic.2007.33.

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Reports on the topic "Algorithme de Baum-Welch"

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Baggenstoss, Paul M. A Baum-Welch Algorithm for Noisy Vector Fields for Classification and Synthesis of Textures Using Non-Symmetric Half-Plane. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada494616.

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