Global ETD Search
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Showing 1 to 20 of 184 for “"hidden markov models"”.
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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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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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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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Massively Parallel Hidden Markov Models for Wireless Applications
… high computational complexity. Additionally, Hidden Markov Models (HMMs) are a widely used mathematical modeling tool used in various fields of engineering and sciences. In electrical and computer engineering, it is used in several areas, including speech recognition, handwriting recognition, …
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Applying binary decision diagrams to learn hidden Markov models
… EM-BDD, an algorithm for learning parameters of Hidden Markov Models, by building upon the Baum-Welch (BW) algorithm’s methodology. EM-BDD utilises the Forward-Backward procedure, a cornerstone of BW, adapting it to operate on Binary Decision Diagrams (BDDs). The time and memory complexity of the …
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Discriminative training of hidden Markov Models for gesture recognition
… problem of modelling temporal data. Non-temporal models can be used for gesture recognition, but require that the signals be adapted to the models. For example, the requirement of fixed-length inputs for support-vector machine classification. Hidden Markov models are probabilistic graphical models …
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Automatic refinement of hidden Markov models for speech recognition
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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