Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 391 for “"Hidden Markov"”.
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Experiments with hidden Markov models
… a report on the results of our experiments with hidden Markov models, focusing on Markov chains with generative transitions and the Baum-Welch algorithm. We explore generating the hypothesis model in various ways. We use the hypothesis model as the original model. And we investigate the …
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Card counting meets hidden Markov models
The Hidden Markov Model (HMM) is a stochastic process that involves an unobservable Markov Chain and an observable output at each state in the chain. Hidden Markov Models are described by three parameters: A, B, and \uf070. A is a matrix that holds the transition probabilities for the unobservable …
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Spectral Estimation of Hidden Markov Models
… methods for estimating key quantities of hidden Markov models through spectral method-of-moments estimation. Unlike traditional estimation methods like EM and Gibbs sampling, the set of estimation methods, which we call spectral HMMs (sHMMs), are incredibly fast, do not require multiple …
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Face recognition using Hidden Markov Models
… recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowledge with statistical information, HMMs can be used successfully to encode face features. The results reported are obtained using a database of images of 40 subjects, …
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Option pricing using hidden Markov models
… a closed-form expression for option prices: the Hidden Markov Option Pricing Model. This is possible due to the macro-structure of this model and provides the added advantage of ensuring efficient computation of option prices. This model turns out to be a very natural extension to the …
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Model reduction for Hidden Markov models
… classes of dynamical systems, finite alphabet Hidden Markov Models and Jump Linear Systems with finite parameter space. The reduction algorithms employ convex optimization and numerical linear algebra tools and do not pose any structural requirements on the systems at hand. In the Jump Linear …
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Hidden Markov models for gesture recognition
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
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Hidden Markov model based visual speech recognition
… processing are presented. Classifiers based on Hidden Markov Model (HMM) are first explored for modeling and identifying the basic visual speech elements. Considering that the temporal features of visual speech elements may be confusable and sensitive to their contexts, three novel training …
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Face recognition using hidden Markov model supervectors
This project attempts to boost the results of face recognition algorithms already established to perform face recognition by augmenting the architecture and using HMM-based supervector classification. In this thesis, the work of Tang’s 2010 dissertation is used such that the HMM based classifier …
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Graphical analysis of hidden Markov model experiments
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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Cellular diagnostic systems using hidden Markov models
… clues about the cause of the failure are hidden deep within the statistics of underlying dynamic physical phenomena like fading, shadowing, and interference. In this research we propose that Hidden Markov Models (HMMs) be used as a method to infer signature statistics about the nature and …
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Administración algorítmica de portafolio con Hidden Markov models
… learning. Se hace énfasis en el uso de los Hidden Markov Models para mejorar la estrategia de trading y la administración del portafolio.
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Autoregressive hidden Markov models and the speech signal
This thesis introduces an autoregressive hidden Markov model (HMM) and demonstrates its application to the speech signal. This new variant of the HMM is built upon the mathematical structure of the HMM and linear prediction analysis of speech signals. By incorporating these two methods into one …
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Two new approaches for learning Hidden Markov Models
Hidden Markov Models (HMMs) are ubiquitously used in applications such as speech recognition and gene prediction that involve inferring latent variables given observations. For the past few decades, the predominant technique used to infer these hidden variables has been the Baum-Welch algorithm. …
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On-line handwriting recognition using hidden Markov models
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Inventory estimation from transactions via hidden Markov models
… inventory tracking in the retail industry using Hidden Markov Models. It has been observed that inventory records are extremely inaccurate in practice (cf. [1{4]). Reasons for this inaccuracy are item losses due to item theft, mishandling, etc. which are unaccounted. Even more important are the …
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Hidden Markov model analysis of subcellular particle trajectories
… the application of several variants of hidden Markov models (HMMs) to analyzing the trajectories of such particles. And we compare the performance of our proposed algorithms with traditional approaches that involve fitting a mean square displacement (MSD) curve calculated from the …
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Automatic Phoneme Recognition with Segmental Hidden Markov Models
… description of the integrated elements. The Hidden Markov Model (HMM) based phoneme models are trained using the Baum-Welch re-estimation procedure. Recognition and segmentation of the phonemes in the continuous speech is performed by a Segmental Viterbi Search on a Segmental Ergodic HMM for …
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