subject:(Gaussian mixtures)

University of Cincinnati

1. Zhao, Nan. Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI.

Degree: PhD, Arts and Sciences: Physics, 2020, University of Cincinnati

URL: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748540796138

► Fast imaging has long been a key direction of magnetic resonance imaging (MRI) research. Rapid imaging technology can not only shorten the measurement time, reducing… (more)

▼ Fast imaging has long been a key direction of magnetic resonance imaging (MRI) research. Rapid imaging technology can not only shorten the measurement time, reducing the time and cost of scientific research, but also reduce patient burden to in clinical applications and reduce some errors caused by longer measurements. At present, some rapid imaging methods have been applied to clinical medicine and have achieved good results. We achieve relatively good experimental results by adjusting the parameters in the MRI sequence based on tissue parameters such as the longitudinal relaxation time, T1, and transverse relaxation time, T2. Common sequence parameters to control contrast are the flip angle, a, echo time (TE), repetition time (TR), or the addition of pre-pulses (inversion, saturation, etc). T1 mapping and T2 mapping are very common imaging methods in MRI. High- contrast T1 and T2 mapping can clearly distinguish different tissues, providing important reference data for histological study and research. The work proposed in this thesis involves combination of fast imaging and classic MRI methods, in order to develop a new mapping method for T1 and T2, so as to obtain a mapping with sufficient accuracy in as short a time as possible. The method we used in our experiment is one of the fastest current approaches: driven-equilibrium single-pulse observation of T1 or T2 (DESPOT1/T2) which is based on making multiple measurements using steady state sequences with TR < T1 or T2. In order to shorten the sampling time, we modified the DESPOT approach to use variable density sampling patterns that allow collecting as little data as possible while maintaining image quality. We also compared multiple image reconstruction methods to find the best experimental method, hoping to improve image quality. The image signal-to-noise ratio is usually adversely affected by a reduction of sampling data. Therefore, finding an accurate noise reduction method, regardless of the specific image acquisition approach is also one of our goals. We will introduce some of the most common noise reduction methods and compare them with a proposed joint multi-channel noise reduction. Because the data we collected are complex, the denoising methods we propose mainly aim at complex-valued volume data composed of multiple observations. The results show that the Bayesian least squares Gaussian scale mixtures (BLS-GSM) denoising method can provide a restored image with low noise and good preservation of image detail. Advisors/Committee Members: Lee, Gregory (Committee Chair), Smith, Leigh (Committee Chair).

Subjects/Keywords: Radiology; fast imaging; DESPOT1T2 mapping; Quantitative MRI; multichannel denoising; Gaussian scale mixtures; Bayesian estimation

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APA (6^th Edition):

Zhao, N. (2020). Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748540796138

Chicago Manual of Style (16^th Edition):

Zhao, Nan. “Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI.” 2020. Doctoral Dissertation, University of Cincinnati. Accessed April 22, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748540796138.

MLA Handbook (7^th Edition):

Zhao, Nan. “Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI.” 2020. Web. 22 Apr 2021.

Vancouver:

Zhao N. Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI. [Internet] [Doctoral dissertation]. University of Cincinnati; 2020. [cited 2021 Apr 22]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748540796138.

Council of Science Editors:

Zhao N. Accelerated T1 and T2 Parameter Mapping and Data Denoising Methods for 3D Quantitative MRI. [Doctoral Dissertation]. University of Cincinnati; 2020. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613748540796138

2. Fernandes maligo, Artur otavio. Unsupervised Gaussian mixture models for the classification of outdoor environments using 3D terrestrial lidar data : Modèles de mélange gaussien sans surveillance pour la classification des environnements extérieurs en utilisant des données 3D de lidar terrestre.

Degree: Docteur es, Robotique, 2016, Toulouse, INSA

URL: http://www.theses.fr/2016ISAT0053

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Le traitement de nuages de points 3D de lidars permet aux robots mobiles autonomes terrestres de construire des modèles sémantiques de l'environnement extérieur dans lequel… (more)

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Le traitement de nuages de points 3D de lidars permet aux robots mobiles autonomes terrestres de construire des modèles sémantiques de l'environnement extérieur dans lequel ils évoluent. Ces modèles sont intéressants car ils représentent des informations qualitatives, et ainsi donnent à un robot la capacité de raisonner à un niveau plus élevé d'abstraction. Le coeur d'un système de modélisation sémantique est la capacité de classifier les observations venant du capteur. Nous proposons un système de classification centré sur l'apprentissage non-supervisé. La prémière couche, la couche intermédiaire, consiste en un modèle de mélange gaussien. Ce modèle est déterminé de manière non-supervisée lors d'une étape de training. Il definit un ensemble de classes intermédiaires qui correspond à une partition fine des classes présentes dans l'environnement. La deuxième couche, la couche finale, consiste en un regroupement des classes intermédiaires dans un ensemble de classes finales qui, elles, sont interprétables dans le contexte de la tâche ciblée. Le regroupement est déterminé par un expert lors de l'étape de training, de manière supervisée, mais guidée par les classes intermédiaires. L'évaluation est basée sur deux jeux de données acquis avec de différents lidars et possédant différentes caractéristiques. L'évaluation est quantitative pour l'un des jeux de données, et qualitative pour l'autre. La concéption du système utilise la procédure standard de l'apprentissage, basée sur les étapes de training, validation et test. L'opération suit la pipeline standard de classification. Le système est simple, et ne requiert aucun pré-traitement ou post-traitement.

The processing of 3D lidar point clouds enable terrestrial autonomous mobile robots to build semantic models of the outdoor environments in which they operate. Such models are interesting because they encode qualitative information, and thus provide to a robot the ability to reason at a higher level of abstraction. At the core of a semantic modelling system, lies the capacity to classify the sensor observations. We propose a two-layer classi- fication model which strongly relies on unsupervised learning. The first, intermediary layer consists of a Gaussian mixture model. This model is determined in a training step in an unsupervised manner, and defines a set of intermediary classes which is a fine-partitioned representation of the environment. The second, final layer consists of a grouping of the intermediary classes into final classes that are interpretable in a considered target task. This grouping is determined by an expert during the training step, in a process which is supervised, yet guided by the intermediary classes. The evaluation is done for two datasets acquired with different lidars and possessing different characteristics. It is done quantitatively using one of the datasets, and qualitatively using another. The system is designed following the standard learning procedure, based on a training, a validation and a test steps. The operation follows a standard…

Advisors/Committee Members: Lacroix, Simon (thesis director).

Subjects/Keywords: Classification non-supervisée; Mélange de gaussiennes; Données 3D Lidar; Gaussian mixtures; Lidar point-clouds; Unsupervised classification; 629.8

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APA (6^th Edition):

Fernandes maligo, A. o. (2016). Unsupervised Gaussian mixture models for the classification of outdoor environments using 3D terrestrial lidar data : Modèles de mélange gaussien sans surveillance pour la classification des environnements extérieurs en utilisant des données 3D de lidar terrestre. (Doctoral Dissertation). Toulouse, INSA. Retrieved from http://www.theses.fr/2016ISAT0053

Chicago Manual of Style (16^th Edition):

Fernandes maligo, Artur otavio. “Unsupervised Gaussian mixture models for the classification of outdoor environments using 3D terrestrial lidar data : Modèles de mélange gaussien sans surveillance pour la classification des environnements extérieurs en utilisant des données 3D de lidar terrestre.” 2016. Doctoral Dissertation, Toulouse, INSA. Accessed April 22, 2021. http://www.theses.fr/2016ISAT0053.

MLA Handbook (7^th Edition):

Fernandes maligo, Artur otavio. “Unsupervised Gaussian mixture models for the classification of outdoor environments using 3D terrestrial lidar data : Modèles de mélange gaussien sans surveillance pour la classification des environnements extérieurs en utilisant des données 3D de lidar terrestre.” 2016. Web. 22 Apr 2021.

Vancouver:

Fernandes maligo Ao. Unsupervised Gaussian mixture models for the classification of outdoor environments using 3D terrestrial lidar data : Modèles de mélange gaussien sans surveillance pour la classification des environnements extérieurs en utilisant des données 3D de lidar terrestre. [Internet] [Doctoral dissertation]. Toulouse, INSA; 2016. [cited 2021 Apr 22]. Available from: http://www.theses.fr/2016ISAT0053.

Council of Science Editors:

Fernandes maligo Ao. Unsupervised Gaussian mixture models for the classification of outdoor environments using 3D terrestrial lidar data : Modèles de mélange gaussien sans surveillance pour la classification des environnements extérieurs en utilisant des données 3D de lidar terrestre. [Doctoral Dissertation]. Toulouse, INSA; 2016. Available from: http://www.theses.fr/2016ISAT0053

3. Sebbar, Mehdi. On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension.

Degree: Docteur es, Mathématiques appliquées, 2017, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2017SACLG003

► Dans ce mémoire de thèse, nous abordons deux thèmes, le clustering en haute dimension d'une part et l'estimation de densités de mélange d'autre part. Le… (more)

▼ Dans ce mémoire de thèse, nous abordons deux thèmes, le clustering en haute dimension d'une part et l'estimation de densités de mélange d'autre part. Le premier chapitre est une introduction au clustering. Nous y présentons différentes méthodes répandues et nous nous concentrons sur un des principaux modèles de notre travail qui est le mélange de Gaussiennes. Nous abordons aussi les problèmes inhérents à l'estimation en haute dimension et la difficulté d'estimer le nombre de clusters. Nous exposons brièvement ici les notions abordées dans ce manuscrit. Considérons une loi mélange de K Gaussiennes dans R^p. Une des approches courantes pour estimer les paramètres du mélange est d'utiliser l'estimateur du maximum de vraisemblance. Ce problème n'étant pas convexe, on ne peut garantir la convergence des méthodes classiques. Cependant, en exploitant la biconvexité de la log-vraisemblance négative, on peut utiliser la procédure itérative 'Expectation-Maximization' (EM). Malheureusement, cette méthode n'est pas bien adaptée pour relever les défis posés par la grande dimension. Par ailleurs, cette méthode requiert de connaître le nombre de clusters. Le Chapitre 2 présente trois méthodes que nous avons développées pour tenter de résoudre les problèmes décrits précédemment. Les travaux qui y sont exposés n'ont pas fait l'objet de recherches approfondies pour diverses raisons. La première méthode, 'lasso graphique sur des mélanges de Gaussiennes', consiste à estimer les matrices inverses des matrices de covariance dans l'hypothèse où celles-ci sont parcimonieuses. Nous adaptons la méthode du lasso graphique de [Friedman et al., 2007] sur une composante dans le cas d'un mélange et nous évaluons expérimentalement cette méthode. Les deux autres méthodes abordent le problème d'estimation du nombre de clusters dans le mélange. La première est une estimation pénalisée de la matrice des probabilités postérieures dont la composante (i,j) est la probabilité que la i-ème observation soit dans le j-ème cluster. Malheureusement, cette méthode s'est avérée trop coûteuse en complexité. Enfin, la deuxième méthode considérée consiste à pénaliser le vecteur de poids afin de le rendre parcimonieux. Cette méthode montre des résultats prometteurs. Dans le Chapitre 3, nous étudions l'estimateur du maximum de vraisemblance d'une densité de n observations i.i.d. sous l’hypothèse qu'elle est bien approximée par un mélange de plusieurs densités données. Nous nous intéressons aux performances de l'estimateur par rapport à la perte de Kullback-Leibler. Nous établissons des bornes de risque sous la forme d'inégalités d'oracle exactes, que ce soit en probabilité ou en espérance. Nous démontrons à travers ces bornes que, dans le cas du problème d’agrégation convexe, l'estimateur du maximum de vraisemblance atteint la vitesse (log K)/n)^1/2, qui est optimale à un terme logarithmique près, lorsque le nombre de composant est plus grand que n^1/2. Plus important, sous l’hypothèse supplémentaire que la matrice de Gram des composantes du dictionnaire satisfait… Advisors/Committee Members: Dalalyan, Arnak S. (thesis director).

Subjects/Keywords: Clustering; Agrégation; Grande dimension; Estimation de densité; Mélange de gaussiennes; Gaussian mixtures; Clustering; High dimension; Density estimation; Aggregation; 519; 62

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sebbar, M. (2017). On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2017SACLG003

Chicago Manual of Style (16^th Edition):

Sebbar, Mehdi. “On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension.” 2017. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed April 22, 2021. http://www.theses.fr/2017SACLG003.

MLA Handbook (7^th Edition):

Sebbar, Mehdi. “On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension.” 2017. Web. 22 Apr 2021.

Vancouver:

Sebbar M. On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2017. [cited 2021 Apr 22]. Available from: http://www.theses.fr/2017SACLG003.

Council of Science Editors:

Sebbar M. On unsupervised learning in high dimension : Sur l'apprentissage non supervisé en haute dimension. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2017. Available from: http://www.theses.fr/2017SACLG003

4. Yu, Jia. Distributed parameter and state estimation for wireless sensor networks.

Degree: PhD, 2017, University of Edinburgh

URL: http://hdl.handle.net/1842/28929

► The research in distributed algorithms is linked with the developments of statistical inference in wireless sensor networks (WSNs) applications. Typically, distributed approaches process the collected… (more)

▼ The research in distributed algorithms is linked with the developments of statistical inference in wireless sensor networks (WSNs) applications. Typically, distributed approaches process the collected signals from networked sensor nodes. That is to say, the sensors receive local observations and transmit information between each other. Each sensor is capable of combining the collected information with its own observations to improve performance. In this thesis, we propose novel distributed methods for the inference applications using wireless sensor networks. In particular, the efficient algorithms which are not computationally intensive are investigated. Moreover, we present a number of novel algorithms for processing asynchronous network events and robust state estimation. In the first part of the thesis, a distributed adaptive algorithm based on the component-wise EM method for decentralized sensor networks is investigated. The distributed component-wise Expectation-Maximization (EM) algorithm has been designed for application in a Gaussian density estimation. The proposed algorithm operates a component-wise EM procedure for local parameter estimation and exploit an incremental strategy for network updating, which can provide an improved convergence rate. Numerical simulation results have illustrated the advantages of the proposed distributed component-wise EM algorithm for both well-separated and overlapped mixture densities. The distributed component-wise EM algorithm can outperform other EM-based distributed algorithms in estimating overlapping Gaussian mixtures. In the second part of the thesis, a diffusion based EM gradient algorithm for density estimation in asynchronous wireless sensor networks has been proposed. Specifically, based on the asynchronous adapt-then-combine diffusion strategy, a distributed EM gradient algorithm that can deal with asynchronous network events has been considered. The Bernoulli model has been exploited to approximate the asynchronous behaviour of the network. Compared with existing distributed EM based estimation methods using a consensus strategy, the proposed algorithm can provide more accurate estimates in the presence of asynchronous networks uncertainties, such as random link failures, random data arrival times, and turning on or off sensor nodes for energy conservation. Simulation experiments have been demonstrated that the proposed algorithm significantly outperforms the consensus based strategies in terms of Mean-Square- Deviation (MSD) performance in an asynchronous network setting. Finally, the challenge of distributed state estimation in power systems which requires low complexity and high stability in the presence of bad data for a large scale network is addressed. A gossip based quasi-Newton algorithm has been proposed for solving the power system state estimation problem. In particular, we have applied the quasi-Newton method for distributed state estimation under the gossip protocol. The proposed algorithm exploits the Broyden- Fletcher-Goldfarb-Shanno (BFGS)…

Subjects/Keywords: distributed algorithms; statistical inference; wireless sensor networks; WSNs applications; EM algorithms; Gaussian mixtures; EM gradient algorithm; Bernoulli model; BFGS formula

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Yu, J. (2017). Distributed parameter and state estimation for wireless sensor networks. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/28929

Chicago Manual of Style (16^th Edition):

Yu, Jia. “Distributed parameter and state estimation for wireless sensor networks.” 2017. Doctoral Dissertation, University of Edinburgh. Accessed April 22, 2021. http://hdl.handle.net/1842/28929.

MLA Handbook (7^th Edition):

Yu, Jia. “Distributed parameter and state estimation for wireless sensor networks.” 2017. Web. 22 Apr 2021.

Vancouver:

Yu J. Distributed parameter and state estimation for wireless sensor networks. [Internet] [Doctoral dissertation]. University of Edinburgh; 2017. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/1842/28929.

Council of Science Editors:

Yu J. Distributed parameter and state estimation for wireless sensor networks. [Doctoral Dissertation]. University of Edinburgh; 2017. Available from: http://hdl.handle.net/1842/28929

5. Anderson, Joseph T. Geometric Methods for Robust Data Analysis in High Dimension.

Degree: PhD, Computer Science and Engineering, 2017, The Ohio State University

URL: http://rave.ohiolink.edu/etdc/view?acc_num=osu1488372786126891

► Data-driven applications are growing. Machine learning and data analysis now finds both scientific and industrial application in biology, chemistry, geology, medicine, and physics. These applications… (more)

▼ Data-driven applications are growing. Machine learning and data analysis now finds both scientific and industrial application in biology, chemistry, geology, medicine, and physics. These applications rely on large quantities of data gathered from automated sensors and user input. Furthermore, the dimensionality of many datasets is extreme: more details are being gathered about single user interactions or sensor readings. All of these applications encounter problems with a common theme: use observed data to make inferences about the world. Our work obtains the first provably efficient algorithms for Independent Component Analysis (ICA) in the presence of heavy-tailed data. The main tool in this result is the centroid body (a well-known topic in convex geometry), along with optimization and random walks for sampling from a convex body. This is the first algorithmic use of the centroid body and it is of independent theoretical interest, since it effectively replaces the estimation of covariance from samples, and is more generally accessible.We demonstrate that ICA is itself a powerful geometric primitive. That is, having access to an efficient algorithm for ICA enables us to efficiently solve other important problems in machine learning. The first such reduction is a solution to the open problem of efficiently learning the intersection of n + 1 halfspaces in Rn, posed in [43]. This reduction relies on a non-linear transformation of samples from such an intersection of halfspaces (i.e. a simplex) to samples which are approximately from a linearly transformed product distribution. Through this transformation of samples, which can be done efficiently, one can then use an ICA algorithm to recover the vertices of the intersection of halfspaces.Finally, we again use ICA as an algorithmic primitive to construct an efficient solution to the widely-studied problem of learning the parameters of a Gaussian mixture model. Our algorithm again transforms samples from a Gaussian mixture model into samples which fit into the ICA model and, when processed by an ICA algorithm, result in recovery of the mixture parameters. Our algorithm is effective even when the number of Gaussians in the mixture grows with the ambient dimension, even polynomially in the dimension. In addition to the efficient parameter estimation, we also obtain a complexity lower bound for a low-dimension Gaussian mixture model. Advisors/Committee Members: Rademacher, Luis (Advisor), Sidiropoulos, Anastasios (Committee Chair).

Subjects/Keywords: Computer Science; Applied Mathematics; Machine Learning, Convex Geometry, Data Analysis, Independent Component Analysis, Gaussian Mixtures, Signal Separation

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APA (6^th Edition):

Anderson, J. T. (2017). Geometric Methods for Robust Data Analysis in High Dimension. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1488372786126891

Chicago Manual of Style (16^th Edition):

Anderson, Joseph T. “Geometric Methods for Robust Data Analysis in High Dimension.” 2017. Doctoral Dissertation, The Ohio State University. Accessed April 22, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488372786126891.

MLA Handbook (7^th Edition):

Anderson, Joseph T. “Geometric Methods for Robust Data Analysis in High Dimension.” 2017. Web. 22 Apr 2021.

Vancouver:

Anderson JT. Geometric Methods for Robust Data Analysis in High Dimension. [Internet] [Doctoral dissertation]. The Ohio State University; 2017. [cited 2021 Apr 22]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1488372786126891.

Council of Science Editors:

Anderson JT. Geometric Methods for Robust Data Analysis in High Dimension. [Doctoral Dissertation]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1488372786126891

6. Khlaifi, Hajer. Preliminary study for detection and classiﬁcation of swallowing sound : Étude préliminaire de détection et classification des sons de la déglutition.

Degree: Docteur es, Bioingénierie et Sciences et Technologies de l’Information et des Systèmes : Unité de Recherche Biomécanique et Bio-ingénierie (UMR-7338), 2019, Compiègne

URL: http://www.theses.fr/2019COMP2485

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Les maladies altérant le processus de la déglutition sont multiples, affectant la qualité de vie du patient et sa capacité de fonctionner en société. La… (more)

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Les maladies altérant le processus de la déglutition sont multiples, affectant la qualité de vie du patient et sa capacité de fonctionner en société. La nature exacte et la gravité des changements post/pré-traitement dépendent de la localisation de l’anomalie. Une réadaptation efficace de la déglutition, cliniquement parlant, dépend généralement de l’inclusion d’une évaluation vidéo-fluoroscopique de la déglutition du patient dans l’évaluation post-traitement des patients en risque de fausse route. La restriction de cette utilisation est due au fait qu’elle est très invasive, comme d’autres moyens disponibles, tels que la fibre optique endoscopique. Ces méthodes permettent d’observer le déroulement de la déglutition et d’identifier les lieux de dysfonctionnement, durant ce processus, avec une précision élevée. "Mieux vaut prévenir que guérir" est le principe de base de la médecine en général. C’est dans ce contexte que se situe ce travail de thèse pour la télésurveillance des malades et plus spécifiquement pour suivre l’évolution fonctionnelle du processus de la déglutition chez des personnes à risques dysphagiques, que ce soit à domicile ou bien en institution, en utilisant le minimum de capteurs non-invasifs. C’est pourquoi le principal signal traité dans ce travail est le son. La principale problématique du traitement du signal sonore est la détection automatique du signal utile du son, étape cruciale pour la classification automatique de sons durant la prise alimentaire, en vue de la surveillance automatique. L’étape de la détection du signal utile permet de réduire la complexité du système d’analyse sonore. Les algorithmes issus de l’état de l’art traitant la détection du son de la déglutition dans le bruit environnemental n’ont pas montré une bonne performance. D’où l’idée d’utiliser un seuil adaptatif sur le signal, résultant de la décomposition en ondelettes. Les problématiques liées à la classification des sons en général et des sons de la déglutition en particulier sont abordées dans ce travail avec une analyse hiérarchique, qui vise à identifier dans un premier temps les segments de sons de la déglutition, puis à le décomposer en trois sons caractéristiques, ce qui correspond parfaitement à la physiologie du processus. Le couplage est également abordé dans ce travail. L’implémentation en temps réel de l’algorithme de détection a été réalisée. Cependant, celle de l’algorithme de classification reste en perspective. Son utilisation en clinique est prévue.

The diseases affecting and altering the swallowing process are multi-faceted, affecting the patient’s quality of life and ability to perform well in society. The exact nature and severity of the pre/post-treatment changes depend on the location of the anomaly. Effective swallowing rehabilitation, clinically depends on the inclusion of a video-fluoroscopic evaluation of the patient’s swallowing in the post-treatment evaluation. There are other available means such as endoscopic optical fibre. The drawback of these evaluation approaches is that they are…

Advisors/Committee Members: Demongeot, Jacques (thesis director), Malouche, Dhafer (thesis director), Istrate, Dan Mircea (thesis director).

Subjects/Keywords: Décomposition en ondelettes; Sons déglutitoires; GMM; HMM; Wavelet decomposition; Signal processing; Detection; Classification; Swallowing; Deglutition disorders; Sound; Gaussian mixtures models (GMM); Hidden Markov models (HMM); Biosensors

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Khlaifi, H. (2019). Preliminary study for detection and classiﬁcation of swallowing sound : Étude préliminaire de détection et classification des sons de la déglutition. (Doctoral Dissertation). Compiègne. Retrieved from http://www.theses.fr/2019COMP2485

Chicago Manual of Style (16^th Edition):

Khlaifi, Hajer. “Preliminary study for detection and classiﬁcation of swallowing sound : Étude préliminaire de détection et classification des sons de la déglutition.” 2019. Doctoral Dissertation, Compiègne. Accessed April 22, 2021. http://www.theses.fr/2019COMP2485.

MLA Handbook (7^th Edition):

Khlaifi, Hajer. “Preliminary study for detection and classiﬁcation of swallowing sound : Étude préliminaire de détection et classification des sons de la déglutition.” 2019. Web. 22 Apr 2021.

Vancouver:

Khlaifi H. Preliminary study for detection and classiﬁcation of swallowing sound : Étude préliminaire de détection et classification des sons de la déglutition. [Internet] [Doctoral dissertation]. Compiègne; 2019. [cited 2021 Apr 22]. Available from: http://www.theses.fr/2019COMP2485.

Council of Science Editors:

Khlaifi H. Preliminary study for detection and classiﬁcation of swallowing sound : Étude préliminaire de détection et classification des sons de la déglutition. [Doctoral Dissertation]. Compiègne; 2019. Available from: http://www.theses.fr/2019COMP2485

7. MENSAH DAVID KWAMENA. VARIATIONAL BAYES METHODS IN GAUSSIAN PROCESS REGRESSION.

Degree: 2015, National University of Singapore

URL: http://scholarbank.nus.edu.sg/handle/10635/124177

Subjects/Keywords: Variational Bayes; Gaussian processes; shape restricted regression; longitudinal data; functional data; functional mixtures

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

KWAMENA, M. D. (2015). VARIATIONAL BAYES METHODS IN GAUSSIAN PROCESS REGRESSION. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/124177

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

KWAMENA, MENSAH DAVID. “VARIATIONAL BAYES METHODS IN GAUSSIAN PROCESS REGRESSION.” 2015. Thesis, National University of Singapore. Accessed April 22, 2021. http://scholarbank.nus.edu.sg/handle/10635/124177.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

KWAMENA, MENSAH DAVID. “VARIATIONAL BAYES METHODS IN GAUSSIAN PROCESS REGRESSION.” 2015. Web. 22 Apr 2021.

Vancouver:

KWAMENA MD. VARIATIONAL BAYES METHODS IN GAUSSIAN PROCESS REGRESSION. [Internet] [Thesis]. National University of Singapore; 2015. [cited 2021 Apr 22]. Available from: http://scholarbank.nus.edu.sg/handle/10635/124177.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

KWAMENA MD. VARIATIONAL BAYES METHODS IN GAUSSIAN PROCESS REGRESSION. [Thesis]. National University of Singapore; 2015. Available from: http://scholarbank.nus.edu.sg/handle/10635/124177

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California

8. Han, Kyu Jeong. Robust speaker clustering under variation in data characteristics.

Degree: PhD, Electrical Engineering, 2009, University of Southern California

URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/279173/rec/5624

► Speaker clustering refers to a process of classifying a set of input speech data (or speech segments) by a speaker identity in an unsupervised way,… (more)

▼ Speaker clustering refers to a process of classifying a set of input speech data (or speech segments) by a speaker identity in an unsupervised way, based on the similarity of speaker-specific characteristics between the data. The process identifies the speech segments of the same speaker source without any prior speaker-specific information of the given input data. This speaker-perspective, unsupervised classification of speech data can be applied as a pre-processing step to speech/speaker recognition or multimedia data segmentation/classification in various ways. Thus, speaker clustering has been recently attracting much attention in the research area of speech recognition and multimedia data processing.; One big, yet unsolved, issue in the research field of speaker clustering is unreliable clustering performance under the variation of input speech data. In this dissertation, we deal with this problem in the framework of agglomerative hierarchical speaker clustering (AHSC) in two perspectives: stopping point estimation and inter-cluster distance measurement. In order to improve the robustness of stopping point estimation for AHSC under the variation of input speech data, we propose a new statistical measure called information change rate (ICR), which can improve estimation of the optimal stopping point. The ICR-based stopping point estimation method is not only empirically but also theoretically verified to be more robust to the variation of input speech data than the conventional BIC-based method. In order to improve the robustness of inter-cluster distance measurement for AHSC under the variation of input speech data, we also propose selective AHSC and incremental Gaussian mixture cluster modeling. These two approaches are proven to provide much more reliability for speaker clustering performance under the variation of input speech data.; Based on these results on robust speaker clustering under the variation of input speech data, we extend our interest to implementing a speaker diarization system, which is more robust to the variation of input audio data. (Speaker diarization refers to an automated process that can annotate a given audio source in terms of "who spoke when".) Focusing on speaker diarization of meeting conversations speech, we propose two refinement schemes to further improve the reliability of speaker clustering performance in the framework of speaker diarization under the variation of input audio data. One is selection of representative speech segments and the other is interaction pattern modeling between meeting participants, and both of them are experimentally verified to enhance the reliability of speaker clustering performance and hence improve the overall diarization accuracy under the variation of input audio data. Advisors/Committee Members: Narayanan, Shrikanth S. (Committee Chair), Kuo, C.-C. Jay (Committee Member), Kang, Hong-Goo (Committee Member), Shahabi, Cyrus (Committee Member).

Subjects/Keywords: incremental gaussian mixtures; information change rate; speaker clustering speaker Diarization; speaker modeling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Han, K. J. (2009). Robust speaker clustering under variation in data characteristics. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/279173/rec/5624

Chicago Manual of Style (16^th Edition):

Han, Kyu Jeong. “Robust speaker clustering under variation in data characteristics.” 2009. Doctoral Dissertation, University of Southern California. Accessed April 22, 2021. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/279173/rec/5624.

MLA Handbook (7^th Edition):

Han, Kyu Jeong. “Robust speaker clustering under variation in data characteristics.” 2009. Web. 22 Apr 2021.

Vancouver:

Han KJ. Robust speaker clustering under variation in data characteristics. [Internet] [Doctoral dissertation]. University of Southern California; 2009. [cited 2021 Apr 22]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/279173/rec/5624.

Council of Science Editors:

Han KJ. Robust speaker clustering under variation in data characteristics. [Doctoral Dissertation]. University of Southern California; 2009. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/279173/rec/5624

9. Houdard, Antoine. Some advances in patch-based image denoising : Quelques avancées dans le débruitage d'images par patchs.

Degree: Docteur es, Traitement du signal et des images, 2018, Université Paris-Saclay (ComUE)

URL: http://www.theses.fr/2018SACLT005

►

Cette thèse s'inscrit dans le contexte des méthodes non locales pour le traitement d'images et a pour application principale le débruitage, bien que les méthodes… (more)

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Cette thèse s'inscrit dans le contexte des méthodes non locales pour le traitement d'images et a pour application principale le débruitage, bien que les méthodes étudiées soient suffisamment génériques pour être applicables à d'autres problèmes inverses en imagerie. Les images naturelles sont constituées de structures redondantes, et cette redondance peut être exploitée à des fins de restauration. Une manière classique d’exploiter cette auto-similarité est de découper l'image en patchs. Ces derniers peuvent ensuite être regroupés, comparés et filtrés ensemble.Dans le premier chapitre, le principe du "global denoising" est reformulé avec le formalisme classique de l'estimation diagonale et son comportement asymptotique est étudié dans le cas oracle. Des conditions précises à la fois sur l'image et sur le filtre global sont introduites pour assurer et quantifier la convergence.Le deuxième chapitre est consacré à l'étude d’a priori gaussiens ou de type mélange de gaussiennes pour le débruitage d'images par patches. Ces a priori sont largement utilisés pour la restauration d'image. Nous proposons ici quelques indices pour répondre aux questions suivantes : Pourquoi ces a priori sont-ils si largement utilisés ? Quelles informations encodent-ils ?Le troisième chapitre propose un modèle probabiliste de mélange pour les patchs bruités, adapté à la grande dimension. Il en résulte un algorithme de débruitage qui atteint les performance de l'état-de-l'art.Le dernier chapitre explore des pistes d'agrégation différentes et propose une écriture de l’étape d'agrégation sous la forme d'un problème de moindre carrés.

This thesis studies non-local methods for image processing, and their application to various tasks such as denoising. Natural images contain redundant structures, and this property can be used for restoration purposes. A common way to consider this self-similarity is to separate the image into "patches". These patches can then be grouped, compared and filtered together.In the first chapter, "global denoising" is reframed in the classical formalism of diagonal estimation and its asymptotic behaviour is studied in the oracle case. Precise conditions on both the image and the global filter are introduced to ensure and quantify convergence.The second chapter is dedicated to the study of Gaussian priors for patch-based image denoising. Such priors are widely used for image restoration. We propose some ideas to answer the following questions: Why are Gaussian priors so widely used? What information do they encode about the image?The third chapter proposes a probabilistic high-dimensional mixture model on the noisy patches. This model adopts a sparse modeling which assumes that the data lie on group-specific subspaces of low dimensionalities. This yields a denoising algorithm that demonstrates state-of-the-art performance.The last chapter explores different way of aggregating the patches together. A framework that expresses the patch aggregation in the form of a least squares problem is proposed.

Advisors/Committee Members: Almansa, Andrés (thesis director), Delon, Julie (thesis director).

Subjects/Keywords: Débruitage d'image; Traitement d'image par patch; Modèles gaussiens; Modèles de mélanges de gaussiennes; Débruitage global; Agrégation de patchs; Image denoising; Patch-based image processing; Gaussian models; Gaussian mixtures models; Global denoising; Patch aggregation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Houdard, A. (2018). Some advances in patch-based image denoising : Quelques avancées dans le débruitage d'images par patchs. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2018SACLT005

Chicago Manual of Style (16^th Edition):

Houdard, Antoine. “Some advances in patch-based image denoising : Quelques avancées dans le débruitage d'images par patchs.” 2018. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed April 22, 2021. http://www.theses.fr/2018SACLT005.

MLA Handbook (7^th Edition):

Houdard, Antoine. “Some advances in patch-based image denoising : Quelques avancées dans le débruitage d'images par patchs.” 2018. Web. 22 Apr 2021.

Vancouver:

Houdard A. Some advances in patch-based image denoising : Quelques avancées dans le débruitage d'images par patchs. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2018. [cited 2021 Apr 22]. Available from: http://www.theses.fr/2018SACLT005.

Council of Science Editors:

Houdard A. Some advances in patch-based image denoising : Quelques avancées dans le débruitage d'images par patchs. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2018. Available from: http://www.theses.fr/2018SACLT005

10. Brkljač Branko. Препознавање облика са ретком репрезентацијом коваријансних матрица и коваријансним дескрипторима.

Degree: 2017, University of Novi Sad

URL: https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija150347865097632.pdf?controlNumber=(BISIS)104951&fileName=150347865097632.pdf&id=10416&source=OATD&language=en ; https://www.cris.uns.ac.rs/record.jsf?recordId=104951&source=OATD&language=en

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У раду је предложен нови модел за ретку апроксимацију Гаусових компоненти у моделима за статистичко препознавање облика заснованим на Гаусовим смешама, а са циљем… (more)

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У раду је предложен нови модел за ретку апроксимацију Гаусових компоненти у моделима за статистичко препознавање облика заснованим на Гаусовим смешама, а са циљем редукције сложености препознавања. Апроксимације инверзних коваријансних матрица конструишу се као ретке линеарне комбинације симетричних матрица из наученог редундантног скупа, коришћењем информационог критеријума који почива на принципу минимума дискриминативне информације. Ретка репрезентација подразумева релативно мали број активних компоненти приликом реконструкције сигнала, а тај циљ постиже тако што истовремено тежи: очувању информационог садржаја и једноставности представе или репрезентације.

U radu je predložen novi model za retku aproksimaciju Gausovih komponenti u modelima za statističko prepoznavanje oblika zasnovanim na Gausovim smešama, a sa ciljem redukcije složenosti prepoznavanja. Aproksimacije inverznih kovarijansnih matrica konstruišu se kao retke linearne kombinacije simetričnih matrica iz naučenog redundantnog skupa, korišćenjem informacionog kriterijuma koji počiva na principu minimuma diskriminativne informacije. Retka reprezentacija podrazumeva relativno mali broj aktivnih komponenti prilikom rekonstrukcije signala, a taj cilj postiže tako što istovremeno teži: očuvanju informacionog sadržaja i jednostavnosti predstave ili reprezentacije.

Paper presents a new model for sparse approximation of Gaussian components in statistical pattern recognition models that are based on Gaussian mixtures, with the aim of reducing computational complexity. Approximations of inverse covariance matrices are designed as sparse linear combinations of symmetric matrices that form redundant set, which is learned through information criterion based on the principle of minimum discrimination information. Sparse representation assumes relatively small number of active components in signal reconstruction, and it achieves that goal by simultaneously striving for: preservation of information content and simplicity of notion or representation.

Advisors/Committee Members: Vukobratović Dejan, Delić Vlado, Crnojević Vladimir, Trpovski Željen, Janev Marko.

Subjects/Keywords: Препознавање облика, коваријансна матрица, Гаусове смеше, реткарепрезентација сигнала, дигитална обрада слике, анализа података; Prepoznavanje oblika, kovarijansna matrica, Gausove smeše, retkareprezentacija signala, digitalna obrada slike, analiza podataka; Pattern recognition, covariance matrix, Gaussian mixtures, sparserepresentation of signals, digital image processing, data analysis

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Branko, B. (2017). Препознавање облика са ретком репрезентацијом коваријансних матрица и коваријансним дескрипторима. (Thesis). University of Novi Sad. Retrieved from https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija150347865097632.pdf?controlNumber=(BISIS)104951&fileName=150347865097632.pdf&id=10416&source=OATD&language=en ; https://www.cris.uns.ac.rs/record.jsf?recordId=104951&source=OATD&language=en

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Branko, Brkljač. “Препознавање облика са ретком репрезентацијом коваријансних матрица и коваријансним дескрипторима.” 2017. Thesis, University of Novi Sad. Accessed April 22, 2021. https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija150347865097632.pdf?controlNumber=(BISIS)104951&fileName=150347865097632.pdf&id=10416&source=OATD&language=en ; https://www.cris.uns.ac.rs/record.jsf?recordId=104951&source=OATD&language=en.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Branko, Brkljač. “Препознавање облика са ретком репрезентацијом коваријансних матрица и коваријансним дескрипторима.” 2017. Web. 22 Apr 2021.

Vancouver:

Branko B. Препознавање облика са ретком репрезентацијом коваријансних матрица и коваријансним дескрипторима. [Internet] [Thesis]. University of Novi Sad; 2017. [cited 2021 Apr 22]. Available from: https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija150347865097632.pdf?controlNumber=(BISIS)104951&fileName=150347865097632.pdf&id=10416&source=OATD&language=en ; https://www.cris.uns.ac.rs/record.jsf?recordId=104951&source=OATD&language=en.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Branko B. Препознавање облика са ретком репрезентацијом коваријансних матрица и коваријансним дескрипторима. [Thesis]. University of Novi Sad; 2017. Available from: https://www.cris.uns.ac.rs/DownloadFileServlet/Disertacija150347865097632.pdf?controlNumber=(BISIS)104951&fileName=150347865097632.pdf&id=10416&source=OATD&language=en ; https://www.cris.uns.ac.rs/record.jsf?recordId=104951&source=OATD&language=en

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

11. Spinnato, Juliette. Modèles de covariance pour l'analyse et la classification de signaux électroencéphalogrammes : Covariance models for electroencephalogramm signals analysis and classification.

Degree: Docteur es, Mathématiques, 2015, Aix Marseille Université

URL: http://www.theses.fr/2015AIXM4727

►

Cette thèse s’inscrit dans le contexte de l’analyse et de la classification de signaux électroencéphalogrammes (EEG) par des méthodes d’analyse discriminante. Ces signaux multi-capteurs qui… (more)

▼

Cette thèse s’inscrit dans le contexte de l’analyse et de la classification de signaux électroencéphalogrammes (EEG) par des méthodes d’analyse discriminante. Ces signaux multi-capteurs qui sont, par nature, très fortement corrélés spatialement et temporellement sont considérés dans le plan temps-fréquence. En particulier, nous nous intéressons à des signaux de type potentiels évoqués qui sont bien représentés dans l’espace des ondelettes. Par la suite, nous considérons donc les signaux représentés par des coefficients multi-échelles et qui ont une structure matricielle électrodes × coefficients. Les signaux EEG sont considérés comme un mélange entre l’activité d’intérêt que l’on souhaite extraire et l’activité spontanée (ou "bruit de fond"), qui est largement prépondérante. La problématique principale est ici de distinguer des signaux issus de différentes conditions expérimentales (classes). Dans le cas binaire, nous nous focalisons sur l’approche probabiliste de l’analyse discriminante et des modèles de mélange gaussien sont considérés, décrivant dans chaque classe les signaux en termes de composantes fixes (moyenne) et aléatoires. Cette dernière, caractérisée par sa matrice de covariance, permet de modéliser différentes sources de variabilité. Essentielle à la mise en oeuvre de l’analyse discriminante, l’estimation de cette matrice (et de son inverse) peut être dégradée dans le cas de grandes dimensions et/ou de faibles échantillons d’apprentissage, cadre applicatif de cette thèse. Nous nous intéressons aux alternatives qui se basent sur la définition de modèle(s) de covariance(s) particulier(s) et qui permettent de réduire le nombre de paramètres à estimer.

The present thesis finds itself within the framework of analyzing and classifying electroencephalogram signals (EEG) using discriminant analysis. Those multi-sensor signals which are, by nature, highly correlated spatially and temporally are considered, in this work, in the timefrequency domain. In particular, we focus on low-frequency evoked-related potential-type signals (ERPs) that are well described in the wavelet domain. Thereafter, we will consider signals represented by multi-scale coefficients and that have a matrix structure electrodes × coefficients. Moreover, EEG signals are seen as a mixture between the signal of interest that we want to extract and spontaneous activity (also called "background noise") which is overriding. The main problematic is here to distinguish signals from different experimental conditions (class). In the binary case, we focus on the probabilistic approach of the discriminant analysis and Gaussian mixtures are used, describing in each class the signals in terms of fixed (mean) and random components. The latter, characterized by its covariance matrix, allow to model different variability sources. The estimation of this matrix (and of its inverse) is essential for the implementation of the discriminant analysis and can be deteriorated by high-dimensional data and/or by small learning samples, which is the application…

Advisors/Committee Members: Torrésani, Bruno (thesis director), Burle, Boris (thesis director).

Subjects/Keywords: Analyse discriminante; Données matricielles; Matrice de covariance séparable; Modèle de mélange gaussien; Modèle linéaire mixte; Décomposition en valeurs singulières; Transformation en ondelettes discrète; Signaux électroencéphalogrammes; Discriminant analysis; Matrix-Based data; Separable covariance matrix; Gaussian mixtures; Linear mixed model; Singular value decomposition; Discrete wavelet transform; Electroencephalogramm signals

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Spinnato, J. (2015). Modèles de covariance pour l'analyse et la classification de signaux électroencéphalogrammes : Covariance models for electroencephalogramm signals analysis and classification. (Doctoral Dissertation). Aix Marseille Université. Retrieved from http://www.theses.fr/2015AIXM4727

Chicago Manual of Style (16^th Edition):

Spinnato, Juliette. “Modèles de covariance pour l'analyse et la classification de signaux électroencéphalogrammes : Covariance models for electroencephalogramm signals analysis and classification.” 2015. Doctoral Dissertation, Aix Marseille Université. Accessed April 22, 2021. http://www.theses.fr/2015AIXM4727.

MLA Handbook (7^th Edition):

Spinnato, Juliette. “Modèles de covariance pour l'analyse et la classification de signaux électroencéphalogrammes : Covariance models for electroencephalogramm signals analysis and classification.” 2015. Web. 22 Apr 2021.

Vancouver:

Spinnato J. Modèles de covariance pour l'analyse et la classification de signaux électroencéphalogrammes : Covariance models for electroencephalogramm signals analysis and classification. [Internet] [Doctoral dissertation]. Aix Marseille Université 2015. [cited 2021 Apr 22]. Available from: http://www.theses.fr/2015AIXM4727.

Council of Science Editors:

Spinnato J. Modèles de covariance pour l'analyse et la classification de signaux électroencéphalogrammes : Covariance models for electroencephalogramm signals analysis and classification. [Doctoral Dissertation]. Aix Marseille Université 2015. Available from: http://www.theses.fr/2015AIXM4727

Linköping University

12. Westberg, Daniel. A sensor fusion method for detection of surface laid land mines.

Degree: Electrical Engineering, 2007, Linköping University

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10479

►

Landminor är ett stort problem både under och efter krigstid. De metoder som används för att detektera minor har inte ändrats mycket sedan 1940-talet.… (more)

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Landminor är ett stort problem både under och efter krigstid. De metoder som används för att detektera minor har inte ändrats mycket sedan 1940-talet. Forskning med mål att utvärdera olika elektro-optiska sensorer och metoder som skulle kunna användas för att skapa mer effektiv min-detektion genomförs på FOI. Försök som har gjorts med data från bland annat laser-radar och IR-sensorer har gett intressanta resultat. I det här examensarbetet utvärderades olika fenomen och egenskaper i laser-radar- och IR-data. De testade egenskaperna var intensitet, IR, ytlikhet och höjd. En metod som segmenterar intressanta objekt och bakgrundsdata utformades och implementerades. Metoden använde sig av expectation-maximization-skattning och ett minimum message length-kriterium. Ett scatter separability-kriterium användes för att bestämma kvalitén på de olika egenskaperna och på den resulterande segmenteringen. Data insamlad under en mätkampanj av FOI användes för att testa metoden. Resultatet visade bland annat att ytlikhetsmåttet gav en bra segmentering för stora objekt med släta ytor, men var sämre för små objekt med skrovliga ytor. Vid jämförelse med en manuellt skapad mål-mask visade det sig att metoden klarade av att välja ut egenskaper som i många fall gav en godkänd segmentering.

Land mines are a huge problem in conflict time and after. Methods used to detect mines have not changed much since the 1940's. Research aiming to evaluate output from different electro-optical sensors and develop methods for more efficient mine detection is performed at FOI. Early experiments with laser radar sensors show promising results, as do analysis of data from infrared sensors. In this thesis, an evaluation is made of features found in laser radar- and in infrared -sensor data. The tested features are intensity, infrared, a surfaceness feature extracted from the laser radar data and height above an estimated ground plane. A method for segmenting interesting objects from background data using theexpectation-maximization algorithm and a minimum message length criterion is designed and implemented. A scatter separability criterion is utilized to determine the quality of the features and the resulting segmentation. The method is tested on real data from a field trial performed by FOI. The results show that the surfaceness feature supports the segmentation of larger object with smooth surfaces but gives no contribution to small object with irregular surfaces. The method produces a decent result of selecting contributing features for different neighbourhoods of a scene. A comparison with a manually created target mask of the neighbourhood and the segmented components show that in most cases a high percentage separation of mine data and background data is possible.

Subjects/Keywords: mine detection; Gaussian mixtures; expectation-maximization; minimum message length criterion; scatter separabilty criterion; infrared; laser radar; Automatic control; Reglerteknik

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Westberg, D. (2007). A sensor fusion method for detection of surface laid land mines. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10479

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Westberg, Daniel. “A sensor fusion method for detection of surface laid land mines.” 2007. Thesis, Linköping University. Accessed April 22, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10479.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Westberg, Daniel. “A sensor fusion method for detection of surface laid land mines.” 2007. Web. 22 Apr 2021.

Vancouver:

Westberg D. A sensor fusion method for detection of surface laid land mines. [Internet] [Thesis]. Linköping University; 2007. [cited 2021 Apr 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10479.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Westberg D. A sensor fusion method for detection of surface laid land mines. [Thesis]. Linköping University; 2007. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10479

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

13. Theeranaew, Wanchat. STUDY ON INFORMATION THEORY: CONNECTION TO CONTROL THEORY, APPROACH AND ANALYSIS FOR COMPUTATION.

Degree: PhD, EECS - System and Control Engineering, 2015, Case Western Reserve University School of Graduate Studies

URL: http://rave.ohiolink.edu/etdc/view?acc_num=case1416847576

► This thesis consists of various studies in information theory, including its connection with control theory and the computational aspects of information measures. The first part… (more)

▼ This thesis consists of various studies in information theory, including its connection with control theory and the computational aspects of information measures. The first part of the research investigates the connection between control theory and information theory. This part extends previous results that mainly focused on this connection in the context of state estimation and feedback control. For linear systems, mutual information, along with the concepts of controllability and observability, is used to derive a tight connection between control theory and information theory. For nonlinear systems, a weaker statement of this connection is established. Some explicit calculations for linear systems and interesting observations about these calculations are presented. The second part investigates the computation of mutual information. An innovative method to compute the mutual information between two collections of time series data based on a Hidden Markov Model (HMM) is proposed. For continuous-valued data, a HMM with Gaussian emission is used to estimate the underlying dynamics of the original data. Mutual information is computed based on the approximate dynamics provided by the HMM. This work improves the estimation of the upper and lower bounds of entropy for Gaussian mixtures, which is one of the key components in this proposed method. This improvement of these bounds are shown to be robust compared to existing methods in all of the synthetic data experiments conducted. In addition, this research includes the study of the computation of Shannon mutual information in which the strong assumptions of independence and identical distribution (i.i.d.) are imposed. This research shows that even if this assumption is violated, the results process a meaningful interpretation. The study of the computation of Shannon mutual information for continuous-valued random variables is included in this research. Three coupled chaotic systems are used as exemplars to show that the computation of normalized mutual information is relatively insensitive to the number of quantized states, although quantization resolution does significantly affect the unnormalized mutual information. The same coupled chaotic systems are used to show that the quantization method also does not significantly affect the normalized mutual information. Simulations from these chaotic systems also show that normalized Shannon mutual information can be used to detect the different (fixed) coupling strengths between two subsystems. Two modified information measures, which enforce sensitivity to time permutation, are compared on these three systems. By using piecewise constant coupling and monotonically decaying coupling, the simulation results show that normalized mutual information can track time-varying changes in coupling strength for these chaos systems to a certain degree. Advisors/Committee Members: Loparo, Kenneth (Advisor).

Subjects/Keywords: Engineering; Mathematics; connection, Control Theory, Information theory, entropy, mutual information, computation, gaussian mixtures, hidden Markov model

…of the upper and lower bounds of entropy for Gaussian mixtures, which is one of the key… …improvement of the computation of entropy and mutual information for Gaussian mixtures is also… …lower bounds of entropy for Gaussian mixtures and our approach to improve these bounds are… …Markov Model (HMM) is proposed. For continuous-valued data, a HMM with Gaussian… …is selected. Because any probability distribution can be approximated by a Gaussian mixture…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Theeranaew, W. (2015). STUDY ON INFORMATION THEORY: CONNECTION TO CONTROL THEORY, APPROACH AND ANALYSIS FOR COMPUTATION. (Doctoral Dissertation). Case Western Reserve University School of Graduate Studies. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1416847576

Chicago Manual of Style (16^th Edition):

Theeranaew, Wanchat. “STUDY ON INFORMATION THEORY: CONNECTION TO CONTROL THEORY, APPROACH AND ANALYSIS FOR COMPUTATION.” 2015. Doctoral Dissertation, Case Western Reserve University School of Graduate Studies. Accessed April 22, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1416847576.

MLA Handbook (7^th Edition):

Theeranaew, Wanchat. “STUDY ON INFORMATION THEORY: CONNECTION TO CONTROL THEORY, APPROACH AND ANALYSIS FOR COMPUTATION.” 2015. Web. 22 Apr 2021.

Vancouver:

Theeranaew W. STUDY ON INFORMATION THEORY: CONNECTION TO CONTROL THEORY, APPROACH AND ANALYSIS FOR COMPUTATION. [Internet] [Doctoral dissertation]. Case Western Reserve University School of Graduate Studies; 2015. [cited 2021 Apr 22]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1416847576.

Council of Science Editors:

Theeranaew W. STUDY ON INFORMATION THEORY: CONNECTION TO CONTROL THEORY, APPROACH AND ANALYSIS FOR COMPUTATION. [Doctoral Dissertation]. Case Western Reserve University School of Graduate Studies; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1416847576

14. Ehsandoust, Bahram. Séparation de Sources Dans des Mélanges non-Lineaires : Blind Source Separation in Nonlinear Mixtures.

Degree: Docteur es, Signal image parole telecoms, 2018, Université Grenoble Alpes (ComUE); Sharif University of Technology (Tehran)

URL: http://www.theses.fr/2018GREAT033

►

La séparation aveugle de sources aveugle (BSS) est une technique d’estimation des différents signaux observés au travers de leurs mélanges à l’aide de plusieurs capteurs,… (more)

▼

La séparation aveugle de sources aveugle (BSS) est une technique d’estimation des différents signaux observés au travers de leurs mélanges à l’aide de plusieurs capteurs, lorsque le mélange et les signaux sont inconnus. Bien qu’il ait été démontré mathématiquement que pour des mélanges linéaires, sous des conditions faibles, des sources mutuellement indépendantes peuvent être estimées, il n’existe dans de résultats théoriques généraux dans le cas de mélanges non-linéaires. La littérature sur ce sujet est limitée à des résultats concernant des mélanges non linéaires spécifiques.Dans la présente étude, le problème est abordé en utilisant une nouvelle approche utilisant l’information temporelle des signaux. L’idée originale conduisant à ce résultat, est d’étudier le problème de mélanges linéaires, mais variant dans le temps, déduit du problème non linéaire initial par dérivation. Il est démontré que les contre-exemples déjà présentés, démontrant l’inefficacité de l’analyse par composants indépendants (ACI) pour les mélanges non-linéaires, perdent leur validité, considérant l’indépendance au sens des processus stochastiques, au lieu de l’indépendance au sens des variables aléatoires. Sur la base de cette approche, de bons résultats théoriques et des développements algorithmiques sont fournis. Bien que ces réalisations ne soient pas considérées comme une preuve mathématique de la séparabilité des mélanges non-linéaires, il est démontré que, compte tenu de quelques hypothèses satisfaites dans la plupart des applications pratiques, elles sont séparables.De plus, les BSS non-linéaires pour deux ensembles utiles de signaux sources sont également traités, lorsque les sources sont (1) spatialement parcimonieuses, ou (2) des processus Gaussiens. Des méthodes BSS particulières sont proposées pour ces deux cas, dont chacun a été largement étudié dans la littérature qui correspond à des propriétés réalistes pour de nombreuses applications pratiques.Dans le cas de processus Gaussiens, il est démontré que toutes les applications non-linéaires ne peuvent pas préserver la gaussianité de l’entrée, cependant, si on restreint l’étude aux fonctions polynomiales, la seule fonction préservant le caractère gaussiens des processus (signaux) est la fonction linéaire. Cette idée est utilisée pour proposer un algorithme de linéarisation qui, en cascade par une méthode BSS linéaire classique, sépare les mélanges polynomiaux de processus Gaussiens.En ce qui concerne les sources parcimonieuses, on montre qu’elles constituent des variétés distinctes dans l’espaces des observations et peuvent être séparées une fois que les variétés sont apprises. À cette fin, plusieurs problèmes d’apprentissage multiple ont été généralement étudiés, dont les résultats ne se limitent pas au cadre proposé du SRS et peuvent être utilisés dans d’autres domaines nécessitant un problème similaire.

Blind Source Separation (BSS) is a technique for estimating individual source components from their mixtures at multiple sensors, where the mixing model is unknown. Although it…

Advisors/Committee Members: Jutten, Christian (thesis director), Babaiezadeh Malmiri, Massoud (thesis director).

Subjects/Keywords: Séparation Aveugle de Sources; Analyse en composantes indépendantes; Mélanges non linéaires; Signaux parcimonieux; Apprentissage sur variétés; Processus Gaussiens; Blind Source Separation; Independent Component Analysis; Nonlinear Mixtures; Sparse Signals; Manifold Learning; Gaussian Processes; 004; 620

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ehsandoust, B. (2018). Séparation de Sources Dans des Mélanges non-Lineaires : Blind Source Separation in Nonlinear Mixtures. (Doctoral Dissertation). Université Grenoble Alpes (ComUE); Sharif University of Technology (Tehran). Retrieved from http://www.theses.fr/2018GREAT033

Chicago Manual of Style (16^th Edition):

Ehsandoust, Bahram. “Séparation de Sources Dans des Mélanges non-Lineaires : Blind Source Separation in Nonlinear Mixtures.” 2018. Doctoral Dissertation, Université Grenoble Alpes (ComUE); Sharif University of Technology (Tehran). Accessed April 22, 2021. http://www.theses.fr/2018GREAT033.

MLA Handbook (7^th Edition):

Ehsandoust, Bahram. “Séparation de Sources Dans des Mélanges non-Lineaires : Blind Source Separation in Nonlinear Mixtures.” 2018. Web. 22 Apr 2021.

Vancouver:

Ehsandoust B. Séparation de Sources Dans des Mélanges non-Lineaires : Blind Source Separation in Nonlinear Mixtures. [Internet] [Doctoral dissertation]. Université Grenoble Alpes (ComUE); Sharif University of Technology (Tehran); 2018. [cited 2021 Apr 22]. Available from: http://www.theses.fr/2018GREAT033.

Council of Science Editors:

Ehsandoust B. Séparation de Sources Dans des Mélanges non-Lineaires : Blind Source Separation in Nonlinear Mixtures. [Doctoral Dissertation]. Université Grenoble Alpes (ComUE); Sharif University of Technology (Tehran); 2018. Available from: http://www.theses.fr/2018GREAT033

15. Pinto, Rafael Coimbra. Online incremental one-shot learning of temporal sequences.

Degree: 2011, Brazil

URL: http://hdl.handle.net/10183/49063

►

Este trabalho introduz novos algoritmos de redes neurais para o processamento online de padrões espaço-temporais, estendendo o algoritmo Incremental Gaussian Mixture Network (IGMN). O algoritmo… (more)

▼

Este trabalho introduz novos algoritmos de redes neurais para o processamento online de padrões espaço-temporais, estendendo o algoritmo Incremental Gaussian Mixture Network (IGMN). O algoritmo IGMN é uma rede neural online incremental que aprende a partir de uma única passada através de dados por meio de uma versão incremental do algoritmo Expectation-Maximization (EM) combinado com regressão localmente ponderada (Locally Weighted Regression, LWR). Quatro abordagens diferentes são usadas para dar capacidade de processamento temporal para o algoritmo IGMN: linhas de atraso (Time-Delay IGMN), uma camada de reservoir (Echo-State IGMN), média móvel exponencial do vetor de entrada reconstruído (Merge IGMN) e auto-referência (Recursive IGMN). Isso resulta em algoritmos que são online, incrementais, agressivos e têm capacidades temporais e, portanto, são adequados para tarefas com memória ou estados internos desconhecidos, caracterizados por fluxo contínuo ininterrupto de dados, e que exigem operação perpétua provendo previsões sem etapas separadas para aprendizado e execução. Os algoritmos propostos são comparados a outras redes neurais espaço-temporais em 8 tarefas de previsão de séries temporais. Dois deles mostram desempenhos satisfatórios, em geral, superando as abordagens existentes. Uma melhoria geral para o algoritmo IGMN também é descrita, eliminando um dos parâmetros ajustáveis manualmente e provendo melhores resultados.

This work introduces novel neural networks algorithms for online spatio-temporal pattern processing by extending the Incremental Gaussian Mixture Network (IGMN). The IGMN algorithm is an online incremental neural network that learns from a single scan through data by means of an incremental version of the Expectation-Maximization (EM) algorithm combined with locally weighted regression (LWR). Four different approaches are used to give temporal processing capabilities to the IGMN algorithm: time-delay lines (Time-Delay IGMN), a reservoir layer (Echo-State IGMN), exponential moving average of reconstructed input vector (Merge IGMN) and self-referencing (Recursive IGMN). This results in algorithms that are online, incremental, aggressive and have temporal capabilities, and therefore are suitable for tasks with memory or unknown internal states, characterized by continuous non-stopping data-flows, and that require life-long learning while operating and giving predictions without separated stages. The proposed algorithms are compared to other spatio-temporal neural networks in 8 time-series prediction tasks. Two of them show satisfactory performances, generally improving upon existing approaches. A general enhancement for the IGMN algorithm is also described, eliminating one of the algorithm’s manually tunable parameters and giving better results.

Advisors/Committee Members: Engel, Paulo Martins.

Subjects/Keywords: Inteligência artificial; Redes neurais; Neural networks; Spatio-temporal pattern processing; Gaussian mixtures; Reservoir computing; Time-series prediction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Pinto, R. C. (2011). Online incremental one-shot learning of temporal sequences. (Masters Thesis). Brazil. Retrieved from http://hdl.handle.net/10183/49063

Chicago Manual of Style (16^th Edition):

Pinto, Rafael Coimbra. “Online incremental one-shot learning of temporal sequences.” 2011. Masters Thesis, Brazil. Accessed April 22, 2021. http://hdl.handle.net/10183/49063.

MLA Handbook (7^th Edition):

Pinto, Rafael Coimbra. “Online incremental one-shot learning of temporal sequences.” 2011. Web. 22 Apr 2021.

Vancouver:

Pinto RC. Online incremental one-shot learning of temporal sequences. [Internet] [Masters thesis]. Brazil; 2011. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/10183/49063.

Council of Science Editors:

Pinto RC. Online incremental one-shot learning of temporal sequences. [Masters Thesis]. Brazil; 2011. Available from: http://hdl.handle.net/10183/49063

16. D. Pandini. STUDI PER LA MODELLIZZAZIONE DELLA RIFLETTANZA SPETTRALE NEGLI STRATI PITTORICI.

Degree: 2012, Università degli Studi di Milano

URL: http://hdl.handle.net/2434/168396

► Nowadays a lot of physical techniques are available in order to have information about an historical painting. They are able to know which chemical elements… (more)

▼ Nowadays a lot of physical techniques are available in order to have information about an historical painting. They are able to know which chemical elements are present in the paint layer, or they are also able to show the pattern under the colored layer, but there is not a non-destructive technique able to study the artist technique of painting or the historical pigments used in order to obtain the particular nuance we can observe. In this thesis we study an evolution of the VIS-NIR-spectroscopic technique with these goals. In particular we start from a preliminary historical study of the artist’s pigments available for paintings and, starting from the colorimetric technique till the spectrophotometetric technique we create a representative pigments Database and we study a new method for pigment grindings identification, pigment’s mixture recognition and pigments layer technique studies. The international method for color measurements provides the use of colorimeter but the sizes of these instruments don’t allow to perform measurements in any cases. Applications in the field of cultural heritage like as pigment characterizations on statues or ceramic and sometimes paintings don’t allow the use of integrating sphere. So we study the applicability of optical fiber for the realization of optical fiber remote probes. The use of optical fibre is consolidated for spectroscopy measurements (in particular for UV-VIS-NIR spectroscopy), but it may introduce an error in the detection of the spectra and, in consequence, a further error in the definition of the color of the sample analyzed. The first goal of this work was therefore the evaluation of compatibility of CIE colorimetric results obtained using Fiber Optic Reflectance Spectroscopy (FORS) and those values obtained with a standard colorimeter and then we extend the use of remote probe in NIR range for the measure of the pigments reflectance spectra. After the test of the remote probe setup we have studied spectra obtained for well-defined mixtures of the most important artist’s pigments with the barium sulphate white pigment, the same used like standard sample for color measurements. These mixture simulate the different desaturation degree of the main color like in the earlier paintings techniques before Renaissance age. In order to have the best numerical characterization we fit the main behavior of the spectra using two analytical models: the Gaussian function (with 4 free parameters) and the sigmoidal function (with 4 free parameters): the comparison of their parameters allow to define the change of the spectra for different concentration of each colored pigment in the white one when thy were mixed in oil or without oil (as in oil renaissance paint technique and in the tipical affresco Middleage paint technique). Determination of weight mixture Pigment to white were: pigment pure, 1:1, 1:2, 1:5, 1:10, 1:20, 1:50. The pigments analyzed with sigmoidal fit were: Cadmium Red , Lacca rossa (Red Laquel), Chromium Orange, Cadmium Yellow, Naple Yellow (with Lead), Green… Advisors/Committee Members: tutore: P. Johnson, N. Ludwig, coordinatore: M. Bersanelli, LUDWIG, NICOLA GHERARDO, BERSANELLI, MARCO RINALDO FEDELE.

Subjects/Keywords: reflectance; pigment; painting; colorimetry; FORS; Kubelka-Munk; scattering coefficient; absorbtion coefficient; azurite; copper green; prussian blue; pigment layer; pigment powder; pigment grinding; spectrophotometer; remote probes; colorimetric values; pigment mixtures; Canaletto; spectrum; glaze; gaussian spectrum fit; sigmoidal spectrum fit; pigment color; pigment tint; optical fiber; colorimeter; Settore FIS/07 - Fisica Applicata(Beni Culturali, Ambientali, Biol.e Medicin)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Pandini, D. (2012). STUDI PER LA MODELLIZZAZIONE DELLA RIFLETTANZA SPETTRALE NEGLI STRATI PITTORICI. (Thesis). Università degli Studi di Milano. Retrieved from http://hdl.handle.net/2434/168396

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Pandini, D.. “STUDI PER LA MODELLIZZAZIONE DELLA RIFLETTANZA SPETTRALE NEGLI STRATI PITTORICI.” 2012. Thesis, Università degli Studi di Milano. Accessed April 22, 2021. http://hdl.handle.net/2434/168396.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Pandini, D.. “STUDI PER LA MODELLIZZAZIONE DELLA RIFLETTANZA SPETTRALE NEGLI STRATI PITTORICI.” 2012. Web. 22 Apr 2021.

Vancouver:

Pandini D. STUDI PER LA MODELLIZZAZIONE DELLA RIFLETTANZA SPETTRALE NEGLI STRATI PITTORICI. [Internet] [Thesis]. Università degli Studi di Milano; 2012. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/2434/168396.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Pandini D. STUDI PER LA MODELLIZZAZIONE DELLA RIFLETTANZA SPETTRALE NEGLI STRATI PITTORICI. [Thesis]. Università degli Studi di Milano; 2012. Available from: http://hdl.handle.net/2434/168396

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

17. Liu, Peng. Adaptive Mixture Estimation and Subsampling PCA.

Degree: PhD, Sciences, 2009, Case Western Reserve University School of Graduate Studies

URL: http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686

► Data mining is important in scientific research, knowledge discovery and decision making. A typical challenge in data mining is that a data set may be… (more)

Subjects/Keywords: Statistics; large data; data mining; mixture models; Gaussian mixtures; parameter estimation; adaptive procedure; partial EM; high-dimensional data; large p small n; dimension reduction; feature selection; subsampling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liu, P. (2009). Adaptive Mixture Estimation and Subsampling PCA. (Doctoral Dissertation). Case Western Reserve University School of Graduate Studies. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686

Chicago Manual of Style (16^th Edition):

Liu, Peng. “Adaptive Mixture Estimation and Subsampling PCA.” 2009. Doctoral Dissertation, Case Western Reserve University School of Graduate Studies. Accessed April 22, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686.

MLA Handbook (7^th Edition):

Liu, Peng. “Adaptive Mixture Estimation and Subsampling PCA.” 2009. Web. 22 Apr 2021.

Vancouver:

Liu P. Adaptive Mixture Estimation and Subsampling PCA. [Internet] [Doctoral dissertation]. Case Western Reserve University School of Graduate Studies; 2009. [cited 2021 Apr 22]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686.

Council of Science Editors:

Liu P. Adaptive Mixture Estimation and Subsampling PCA. [Doctoral Dissertation]. Case Western Reserve University School of Graduate Studies; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1220644686

Erasmus University Rotterdam

18. Ruseckaite, Aiste. New Flexible Models and Design Construction Algorithms for Mixtures and Binary Dependent Variables.

Degree: Department of Econometrics, 2017, Erasmus University Rotterdam

URL: http://hdl.handle.net/1765/94978

► markdownabstractThis thesis discusses new mixture(-amount) models, choice models and the optimal design of experiments. Two chapters of the thesis relate to the so-called mixture, which… (more)

Subjects/Keywords: Choice experiment; Mixture coordinate-exchange algorithm; Particle swarm optimization; Mixture experiment; Ingredient proportions; Gaussian process prior; Nonparametric Bayes; Mixtures of ingredients; Latent environmental consciousness; Eleectric vehicle; Hybrid; Integrated choice and latent variable model (ICLV)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ruseckaite, A. (2017). New Flexible Models and Design Construction Algorithms for Mixtures and Binary Dependent Variables. (Doctoral Dissertation). Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/94978

Chicago Manual of Style (16^th Edition):

Ruseckaite, Aiste. “New Flexible Models and Design Construction Algorithms for Mixtures and Binary Dependent Variables.” 2017. Doctoral Dissertation, Erasmus University Rotterdam. Accessed April 22, 2021. http://hdl.handle.net/1765/94978.

MLA Handbook (7^th Edition):

Ruseckaite, Aiste. “New Flexible Models and Design Construction Algorithms for Mixtures and Binary Dependent Variables.” 2017. Web. 22 Apr 2021.

Vancouver:

Ruseckaite A. New Flexible Models and Design Construction Algorithms for Mixtures and Binary Dependent Variables. [Internet] [Doctoral dissertation]. Erasmus University Rotterdam; 2017. [cited 2021 Apr 22]. Available from: http://hdl.handle.net/1765/94978.

Council of Science Editors:

Ruseckaite A. New Flexible Models and Design Construction Algorithms for Mixtures and Binary Dependent Variables. [Doctoral Dissertation]. Erasmus University Rotterdam; 2017. Available from: http://hdl.handle.net/1765/94978

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