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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Carvajal, Rodrigo Godoy, Boris I. Aguero, Juan C. Yuz, Juan I. Creixell, Werner Univ Tecn Federico Santa Maria Elect Engn Dept Valparaiso Chile Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW Australia

ISBN: (纸本)9789860334074

In this paper we address the joint estimation of the channel impulse response in orthogonal frequency division multiplexing systems with phase distortion, namely phase noise and carrier frequency offset, phase noise bandwidth and the additive noise variance. The estimation algorithm is based on an implementation of the Extended Kalman Filter within the general framework of the expectation-maximization algorithm. We focus on the partial training case, where the transmitted signal is not fully known. To tackle this problem, we utilize a Rao-Blackwellized Extended Kalman Filter. We also compare our results with another nonlinear filtering technique, namely Rao-Blackwellized Particle Filtering, applied to this joint estimation problem. The performance of the two filtering techniques considered in this paper is evaluated in terms of the mean square error of the channel estimates and the numerical complexity introduced by each of these techniques.

关键词： Kalman filters OFDM modulation channel estimation computational complexity expectation-maximisation algorithm mean square error methods optimisation particle filtering (numerical methods) phase noise transient response EM-based ML channel estimation OFDM systems RB-EKF method Rao-Blackwellized extended Kalman filter carrier frequency offset channel impulse response expectation-maximization algorithm maximum likelihood estimation mean square error nonlinear filtering technique orthogonal frequency division multiplexing systems phase distortion phase noise bandwidth Channel estimation Complexity theory Estimation OFDM Training Vectors Mean square error Orthogonal frequency division multiplexing expectation-maximisation algorithm Phase distortion channel impulse response channel estimation Kalman filters Phase noise carrier frequency offset Complexity theory maximum likelihood estimation TRANSIENT RESPONSE complexity classes Functional training

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An integrated framework for reducing hospital readmissions using risk trajectories characterization and discharge timing optimization

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IISE Transactions on Healthcare Systems Engineering 2019年第2期9卷 172-185页

作者： Alaeddini, Adel Helm, Jonathan E. Shi, Pengyi Faruqui, Syed Hasib Akhter Department of Mechanical Engineering University of Texas at San Antonio San Antonio TX United States Department of Operations and Decision Technologies Kelley School of Business Indiana University Bloomington IN United States Krannert School of Management Purdue University West Lafayette IN United States

When patients leave the hospital for lower levels of care, they experience a risk of adverse events on a daily basis. The advent of value-based purchasing among other major initiatives has led to an increasing emphasis on reducing the occurrences of these post-discharge adverse events. This has spurred the development of new prediction technologies to identify which patients are at risk for an adverse event as well as actions to mitigate those risks. Those actions include pre-discharge and post-discharge interventions to reduce risk. However, traditional prediction models have been developed to support only post-discharge actions, predicting risk of adverse events at the time of discharge only. In this article, we develop an integrated framework of risk prediction and discharge optimization that supports both types of interventions: discharge timing and post-discharge monitoring. Our method combines a kernel approach for capturing the nonlinear relationship between length of stay and risk of an adverse event, with a Principle Component Analysis method that makes the resulting estimation tractable. We then demonstrate how this prediction model could be used to support both types of interventions by developing a simple and easily implementable discharge timing optimization. © 2019, © 2019 “IISE”.

关键词： Cox Mixture Model discharge decision optimization expectation-maximization algorithm kernel PCA readmission predication

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Multivariate asset return prediction with mixture models

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EUROPEAN JOURNAL OF FINANCE 2015年第13-14期21卷 1214-1252页

作者： Paolella, Marc S. Univ Zurich Swiss Banking Inst CH-8006 Zurich Switzerland Swiss Finance Inst CH-8006 Zurich Switzerland

The use of mixture distributions for modeling asset returns has a long history in finance. New methods of demonstrating support for the presence of mixtures in the multivariate case are provided. The use of a two-component multivariate normal mixture distribution, coupled with shrinkage via a quasi-Bayesian prior, is motivated, and shown to be numerically simple and reliable to estimate, unlike the majority of multivariate GARCH models in existence. Equally important, it provides a clear improvement over use of GARCH models feasible for use with a large number of assets, such as constant conditional correlation, dynamic conditional correlation, and their extensions, with respect to out-of-sample density forecasting. A generalization to a mixture of multivariate Laplace distributions is motivated via univariate and multivariate analysis of the data, and an expectation-maximization algorithm is developed for its estimation in conjunction with a quasi-Bayesian prior. It is shown to deliver significantly better forecasts than the mixed normal, with fast and numerically reliable estimation. Crucially, the distribution theory required for portfolio theory and risk assessment is developed.

关键词： density forecasting expectation-maximization algorithm fat tails mixture distributions multivariate Laplace distribution quasi-Bayesian estimation shrinkage estimation weighted likelihood

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A mixture of experts network structure for breast cancer diagnosis

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Journal of Medical Systems 2005年第5期29卷 569-579页

作者： Übeyli, Elif Derya Department of Electrical and Electronics Engineering Faculty of Engineering TOBB Ekonomi Ve Teknoloji Üniversitesi Çankaya Ankara Sogutozu 06560 Turkey

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide diagnosing of breast cancer. expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. Specifically, diagnosis is an attempt to accurately forecast the outcome of a specific situation, using as input information obtained from a concrete set of variables that potentially describe the situation. The ME network structure was implemented for breast cancer diagnosis using the attributes of each record in the Wisconsin breast cancer database. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. For the Wisconsin breast cancer diagnosis problem, the obtained total classification accuracy by the ME network structure was 98.85%. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models. © 2005 Springer Science+Business Media, Inc.

关键词： Breast cancer diagnosis Diagnostic accuracy expectation-maximization algorithm Mixture of experts

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Autocorrelation Features for Synthetic Aperture Sonar Image Seabed Segmentation

Autocorrelation Features for Synthetic Aperture Sonar Image ...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： Cobb, J. Tory Principe, Jose USN Warfare Ctr Panama City Div Panama City FL USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

ISBN: (纸本)9781457706530

High-resolution synthetic aperture sonar (SAS) systems yield richly detailed images of seabed environments. algorithms that automatically segment and label seabed textures such as coral, sea grass, sand ripple, and mud, require suitable features that discriminate between the texture classes. Here we present a robust, parameterized SAS image texture model based on the autocorrelation function (ACF) of the intensity image. This ACF texture model has been shown to accurately model first-and second-order statistical features of various seabed environments. An unsupervised multi-class k-means segmentation algorithm that uses the features derived from the ACF model is employed to label rock and ripple textures from a set of textured SAS images. The results of the segmentation are compared against the performance of the segmentation approach using biorthogonal wavelets and Haralick features. In the described experiments, the ACF model features are shown to produce better segmentations than the features based on wavelet coefficients and Haralick features for classifiers of low complexity.

关键词： Autocorrelation expectation-maximization algorithm image segmentation synthetic aperture sonar wavelet coefficients

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Maximum Likelihood Estimation of the Flow Size Distribution Tail Index from Sampled Packet Data 09

Maximum Likelihood Estimation of the Flow Size Distribution ...

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Joint International Conference on Measurement and Modeling of Computer Systems

作者： Loiseau, Patrick Goncalves, Paulo Girard, Stephane Forbes, Florence Primet, Pascale Vicat-Blanc Univ Lyon ENS Lyon Lyon France

ISBN: (纸本)9781605585116

In the context of network traffic analysis, we address the problem of estimating the tail index of flow (or more generally of any group) size distribution from the observation of a, sampled population of packets (individuals). We give an exhaustive bibliography of the existing methods and show the relations between them. The main contribution of this work is then to propose a new method to estimate the tail index from sampled data, based on the resolution of the maximum likelihood problem. To assess the performance of our method, we present a full performance evaluation based on numerical simulations, and also on a real traffic trace corresponding to internet traffic recently acquired.

关键词： Network Monitoring Traffic Measurement Heavy-Tailed Distribution Packet Sampling Maximum Likelihood Estimation expectation-maximization algorithm

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Identification of Nuclear Magnetic Resonance Signals via Gaussian Mixture Decomposition

Identification of Nuclear Magnetic Resonance Signals via Gau...

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10th International Symposium on Intelligent Data Analysis

作者： Krone, Martin Klawonn, Frank Luehrs, Thorsten Ritter, Christiane Ostfalia Univ Appl Sci Dept Comp Sci Salzdahlumer Str 46-48 D-38302 Wolfenbuettel Germany Helmholtz Ctr Infect Res Div Struct Biol D-38124 Braunschweig Germany

ISBN: (纸本)9783642247996

Nuclear Magnetic Resonance spectroscopy is a powerful technique for the determination of protein structures and has been supported by computers for decades. One important step during this process is the identification of resonances in the data. However, due to noise, overlapping effects and artifacts occuring during the measurements, many algorithms fail to identify resonances correctly. In this paper, we present a novel interpretation of the data as a sample drawn from a mixture of bi-variate Gaussian distributions. Therefore, the identification of resonances can be reduced to a Gaussian mixture decomposition problem which is solved with the help of the expectation-maximization algorithm. A program in the Java programming language that exploits an implementation of this algorithm is described and tested on experimental data. Our results indicate that this approach offers valuable information such as an objective measure on the likelihood of the identified resonances.

关键词： Nuclear Magnetic Resonance spectroscopy Gaussian mixture decomposition expectation-maximization algorithm

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Modeling Multi-source Remote Sensing Image Classifier Based on the MDL Principle: Experimental Studies

Modeling Multi-source Remote Sensing Image Classifier Based ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xia, Huaiying Hu, Rukun Xu, Bingxin Guo, Ping Beijing Normal Univ Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China

ISBN: (纸本)9781424465880

In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based on the minimum description length (MDL) principle with mixture model is analyzed theoretically. In this work, experimental studies are performed for investigating the modeling technique. With intensive experiments and sophisticated analysis, it is found that the developed modeling technique can build a robust classification system, which can avoid classifier over-fitting training data and make the learning process trade-off between bias and variance. Meanwhile, designed mixture model is more efficient to represent real multi-source remote sensing images compared to single model.

关键词： minimum description length remote sensing image classification technique mixture model expectation-maximization algorithm

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GAUSSIAN MODELING OF MIXTURES OF NON-STATIONARY SIGNALS IN THE TIME-FREQUENCY DOMAIN (HR-NMF)

GAUSSIAN MODELING OF MIXTURES OF NON-STATIONARY SIGNALS IN T...

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IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

作者： Badeau, Roland Telecom ParisTech Inst Telecom CNRS LTCI F-75634 Paris 13 France

ISBN: (纸本)9781457706936

Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing mixtures of non-stationary signals in the Time-Frequency (TF) domain. However, unlike the High Resolution (HR) methods dedicated to mixtures of exponentials, its spectral resolution is limited by that of the underlying TF representation. In this paper, we propose a unified probabilistic model called HR-NMF, that permits to overcome this limit by taking both phases and local correlations in each frequency band into account. This model is estimated with a recursive implementation of the EM algorithm, that is successfully applied to source separation and audio inpainting.

关键词： Nonnegative Matrix Factorization High Resolution methods expectation-maximization algorithm source separation audio inpainting

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A Bayesian Model for Opening Prediction in RTS Games with Application to StarCraft

A Bayesian Model for Opening Prediction in RTS Games with Ap...

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IEEE Conference on Computational Intelligence and Games (CIG)

作者： Synnaeve, Gabriel Bessiere, Pierre

ISBN: (纸本)9781457700095

This paper presents a Bayesian model to predict the opening (first strategy) of opponents in real-time strategy (RTS) games. Our model is general enough to be applied to any RTS game with the canonical gameplay of gathering resources to extend a technology tree and produce military units and we applied it to StarCraft1. This model can also predict the possible technology trees of the opponent, but we will focus on openings here. The parameters of this model are learned from replays (game logs), labeled with openings. We present a semi-supervised method of labeling replays with the expectation-maximization algorithm and key features, then we use these labels to learn our parameters and benchmark our method with cross-validation. Uses of such a model range from a commentary assistant (for competitive games) to a core component of a dynamic RTS bot/AI, as it will be part of our StarCraft AI competition entry bot.

关键词： Bayes methods Bayesian methods Bayesian model Buildings Games Hidden Markov models Labeling Predictive models RTS game StarCraft commentary assistant competitive game computer games expectation-maximisation algorithm expectation-maximization algorithm opening prediction real-time strategy game semisupervised method

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