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.
Results
Showing 1 to 20 of 74 for “"Expectation-Maximization (EM)"”.
-
Using the EM Algorithm to Estimate the Difference in Dependent Proportions in a 2 x 2 Table with Missing Data.
… table when there are missing data. The Expectation-Maximization (EM) algorithm is used to obtain an estimate for the difference between correlated proportions. To obtain the standard error of this difference I employ a resampling technique known as bootstrapping. The performance of the …
-
Application of the EM Algorithm for Mixture Models
… that identifies trajectories by using the Expectation-Maximization (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algo rithms (e.g. SAS procedure PROC TRAJ) and standard EM, as …
-
Estimation and tracking of rapidly time-varying broadband acoustic communication channels
… on the Extended Kalman Filter (EKF) and the Expectation Maximization (EM) approach respectively are developed.
-
Unsupervised modeling of latent topics and lexical units in speech audio
… discovers important words, phrases and topical themes present in an audio corpus. This system employs a segmental dynamic time warping (S-DTW) algorithm for acoustic pattern discovery in conjunction with a probabilistic model which treats the topic and pseudo-word identity of each discovered …
-
Exploring Statistic Features for Computationally-Efficient Maximum-Likelihood Algorithms in Signal Processing and Communications Applications
The problem of estimating the parameters in a Gaussian mixture probability density function has been prevalent in the literature for nearly a century. During the last two decades, the method of maximum likelihood has become the predominant approach to this problem. In this thesis work, we try to …
-
Accelerated clustering through locality-sensitive hashing
… for two algorithms for clustering data: the expectation-maximization (EM) algorithm and Lloyd's algorithm. The EM algorithm is a heuristic for finding a mixture of k normal distributions in Rd that maximizes the probability of drawing n given data points. Lloyd's algorithm is a special case …
-
Planar detection using modified expectation maximization
… an image pair is cast as an incomplete data problem where the parameters to be estimated are the ones that define the homographies induced by the planar regions in the scene. This incomplete data problem motivates the employment of the Expectation Maximization (EM) algorithm. Derivation of the EM …
-
An EM algorithm for Lidar deconvolution
… return from the observed return signal. The expectation-maximization (EM) algorithm has been used in signal deconvolution, to produce a maximum-likelihood estimate (MLE) for the original signal. We explain the benefits of the EM algorithm over other benchmark algorithms in Lidar …
-
International migration flow table estimation
… in definitions and data collection systems. In this thesis, reported counts are harmonized using correction factors estimated from a constrained optimization procedure. Factors are applied to scale data known to be of a reliable standard, creating an incomplete migration flow table of …
-
Discriminative, generative, and imitative learning
… machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and …
-
Automatic Phoneme Recognition with Segmental Hidden Markov Models
A speaker independent continuous speech phoneme recognition and segmentation system is presented. We discuss the training and recognition phases of the phoneme recognition system as well as a detailed description of the integrated elements. The Hidden Markov Model (HMM) based phoneme models are …
-
Machine Learning to Predict Warhead Fragmentation In-Flight Behavior from Static Data
… Gaussian mixture models (GMMs), fit via expectation maximization (EM), are used to model fragment track intersections on a defined surface of intersection. After modeling the fragment distributions, k-nearest neighbor (K-NN) regressors are used to predict the desired fragmentation …
-
Knowledge discovery and modeling in genomic databases
… acceptor sites in vertebrate genes. The HMM system based on the developed models is fully trained using an expectation maximization (EM) algorithm and the system performance is evaluated using a 10-way cross-validation method. Experimental results show that the proposed HMM system achieves high …
-
Estimation and tracking of rapidly time-varying broadband acoustic communication channels
… on the Extended Kalman Filter (EKF) and the Expectation Maximization (EM) approach respectively are developed. Analysis shows conceptual parallels, including an identical second-order innovation form shared by the EKF modification and the suboptimal EM, and the shared issue of parameter …
-
A Study of Reject Inference Techniques
… average. To estimate these parameters, we usethe Expectation-Maximization (EM) algorithm wherein the data associated with therejected applicants is treated as missing completely at random. Simulated data derivedfrom actual case studies are used to assess the effectiveness of the mixture …
-
A Minimax Approach for Learning Gaussian Mixtures
… a non-convex concave minimax optimization problem. We show that a Gradient Descent Ascent (GDA) method converges to an approximate stationary minimax point of the GAT-GMM optimization problem. In the benchmark case of a mixture of two symmetric, well-separated Gaussians, we further show this …
-
Domain Adaptation with a Classifier Trained by Robust Pseudo-Labels
… based on deep learning algorithms have achieved remarkable results in solving computer vision classification problems. These performance improvements are achieved by assuming the source and target data are collected from the same probability distribution. However, this assumption is usually too …
-
Instrument Timbres and Pitch Estimation in Polyphonic Music
… genre, or meter. Score transcription systems strive for an abstract, compressed form of a piece of music understandable by composers and musicians. Much research still has to be performed to achieve these goals. This thesis connects essential knowledge about music and human auditory …
-
Event detection in EEG signals for brain computer interface using expectation-maximation algorithm
… electroencephalography (EEG)) and translating them into machine-understandable language. Most of the current BCI systems identify features of the brain signals and classify them according to a predefined criterion set by the classifying algorithm. The features of the brain signals can change over …
-
Blind separation of noisy multivariate data using second-order statistics
… and 2) the values of AP, G, and W using the Expectation-Maximization (EM) algorithm, where W is white noise and G is diagonal. The final step estimates A and the set of k underlying sources P using a variant of the joint diagonalization method, where P has k independent unit-variance …
Page 1 of 4