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 47 for “"Expectation-Maximization algorithm"”.
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Multiresolution laser radar range profiling with the expectation-maximization algorithm
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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The Application of the Expectation-Maximization Algorithm to the Identification of Biological Models
… the data and utilize a modification of the Expectation-Maximization algorithm for training it. With our model, we explore some commonly accepted assumptions concerning sampling, discretization, and state transformations. Also, we illuminate the model complexities and interpretation …
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A Fast Clustering Algorithm Merging The Expectation Maximization Algorithm and Markov Chain Monte Carlo
… Learning that is usually solved using Likelihood Maximization methods, of which the Expectation-Maximization algorithm (EM) is the most common. In this work we present an algorithm merging Markov Chain Monte Carlo methods with the EM algorithm to find qualitatively better solutions for the …
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Parameter Estimation for Normally Distributed Grouped Data and Clustering Single-Cell RNA Sequencing Data via the Expectation-Maximization Algorithm
The Expectation-Maximization (EM) algorithm is an iterative algorithm for finding the maximum likelihood estimates in problems involving missing data or latent variables. The EM algorithm can be applied to problems consisting of evidently incomplete data or missingness situations, such as truncated …
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Distributed Target Tracking and Synchronization in Wireless Sensor Networks
… a combination of hierarchical clustering and the expectation-maximization algorithm. Using numerical examples, we show that the proposed distributed particle filtering algorithm improves the accuracy and communication efficiency of distributed target tracking, and that the proposed adaptive …
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Knowledge Distillation for Interpretable Clinical Time Series Outcome Prediction
… predictions is necessary for these models and algorithms to be used in the real world. In this thesis, we use knowledge distillation, which is a technique for taking a model with high predictive power (known as the "teacher model"), and using it to train a model that has other desirable traits …
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Exploiting spatial and spectral information for audio source separation and speaker diarization
… sense of Maximum-Likelihood using a Generalized Expectation-Maximization algorithm by applying supervised Nonnegative Matrix and Tensor Factorization, given spectral descriptions of the source signals. Three modalities of making the descriptions available are addressed, i.e. the descriptions are …
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Hidden Markov Models for Heart Rate Variability with Biometric Applications
… parameterizing the model. The forward-backward algorithm and expectation-maximization algorithm are used to estimate the model and the hidden states for a given observation sequence of inter-beat intervals. Multiple initialization techniques are presented to avoid local maxima. Model order is …
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Generative Models for Computer Vision
In order to build robust computer vision algorithms, scene models are necessary that are capable of capturing various aspects of the data at the same time. These models should be fairly simple, but capable of adapting to the data. Flexible models, as defined in the machine learning community, are …
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Some Advanced Semiparametric Single-index Modeling for Spatially-Temporally Correlated Data
… effects. We estimate these two models using two algorithms based on Markov Chain Expectation Maximization algorithm. Our approaches are compared using simulations, suggesting that the semiparametric single index nonadditive model provides more accurate estimates of spatial correlation. The …
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Prior information for brain parcellation
… boundaries so that standard image analysis algorithms perform poorly. Instead, neuroscientists rely on manual procedures, which are time consuming and increase risks related to inter- and intra-observer reliability [53]. In order to automate this task, we develop an algorithm that robustly …
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Parameter estimation in HMMs with guaranteed convergence
The EM (Expectation-Maximization) algorithm is a heuristic for parameter estimation in statistical models with latent variables, where explicit computation of the maximum likelihood estimate (MLE) is infeasible. Although widely used in practice, the theoretical guarantees associated with EM are …
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Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm
… music. Additionally, a novel method based on the Expectation-Maximization algorithm is used to extract features for classification from the histograms.
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A Two-Parameter Lindley-Binomial Distribution: Properties and Applications
… of moments, maximum like- lihood estimation and expectation-maximization algorithm. This proposed distribution is used to fit two real data sets and to test its goodness of fit. We also compared the performance of proposed distribution with that of bi- nomial and beta-binomial distributions. The …
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Capturing Distributions over Worlds for Robotics with Spatial Scene Grammars
… in the model to data via an approximate expectation-maximization algorithm.
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Clustering in Multidimensional Spaces with Applications to Statistics Analysis of Earthquake Clustering
… is improved with the implementation of an Expectation-Maximization algorithm that is able to automatically detect the two populations in an earthquake catalog and reset the separating threshold for nearest-neighbor earthquake distance according to the parameters of the specific catalog. …
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Techniques for Reliability and Robustness in Integrated Electronic and Photonic Systems
… evolution framework and a boundconstrained expectation maximization algorithm are developed; both these approaches significantly outperform the gradient-based L-BFGS-B algorithm. New schemes for strategic failure analysis on a subset of the failed units are presented, both for detecting the …
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Quantitative analysis of cerebral white matter anatomy from diffusion MRI
In this thesis we develop algorithms for quantitative analysis of white matter fiber tracts from diffusion MRI. The presented methods enable us to look at the variation of a diffusion measure along a fiber tract in a single subject or a population, which allows important clinical studies toward …
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Supervised Classification Using Copula and Mixture Copula
… estimation methods are not suitable and regular expectation maximization algorithm may not converge, and if it does, not efficiently. We propose a new estimation method to evaluate such densities and build the classifier based on finite mixture of copula densities. We develop simulations …
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Network Anomaly Detection with Incomplete Audit Data
… Lastly, this dissertation also proposes an expectation-maximization algorithm based anomaly detection scheme that uses the sampled audit data to detect intrusions in the incoming network traffic.
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