检索结果-内蒙古大学图书馆

27th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Guler, Inan Ubeyli, Elif Derya Guler, Nihal Fatma Gazi Univ Fac Tech Educ Dept Elect & Comp Educ TR-06500 Ankara Turkey

ISBN: (纸本)0780387406

This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for classification of electroencephalogram (EEG) signals. 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. The EEG signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structure was implemented for classification of the EEG signals using the statistical features as inputs. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 93.17% by the ME network structure. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.

关键词： mixture of experts expectation-maximization algorithm discrete wavelet transform EEG signals classification

来源：评论

学校读者我要写书评

暂无评论

Combining parametric and non-parametric algorithms for a partially unsupervised classification of multitemporal remote-sensing images

引用

Information Fusion 2002年第4期3卷 289-297页

作者： Bruzzone, Lorenzo Cossu, Roberto Vernazza, Gianni Department of Information and Communication Technologies University of Trento I-38050 Povo Trento Via Sommarive 14 Italy

In this paper, we propose a classification system based on a multiple-classifier architecture, which is aimed at updating land-cover maps by using multisensor and/or multisource remote-sensing images. The proposed system is composed of an ensemble of classifiers that, once trained in a supervised way on a specific image of a given area, can be retrained in an unsupervised way to classify a new image of the considered site. In this context, two techniques are presented for the unsupervised updating of the parameters of a maximum-likelihood classifier and a radial basis function neural-network classifier, on the basis of the distribution of the new image to be classified. Experimental results carried out on a multitemporal and multisource remote-sensing data set confirm the effectiveness of the proposed system. © 2002 Elsevier Science B.V. All rights reserved.

关键词： expectation-maximization algorithm Maximum-likelihood classifier Multiple-classifier system Radial basis function neural network Unsupervised retraining algorithm

来源：评论

学校读者我要写书评

暂无评论

Condition prognosis of mechanical drives based on nonlinear dynamical models

Condition prognosis of mechanical drives based on nonlinear ...

引用

Conference on Control and Fault-Tolerant Systems (SysTol)

作者： Gasperin, Matej Juricic, Dani Boskoski, Pavle Jozef Stefan Inst Dept Syst & Control Jamova 39 Ljubljana Slovenia

ISBN: (纸本)9781424481545

Forecasting the condition of the equipment is becoming an important ingredient of the advanced maintenance and asset management systems. In this paper a probabilistic approach to the prognosis of damage progression in gearboxes is presented. It is based on a stochastic nonlinear grey-box model of the underlying wear phenomena. Model parameters are estimated from the available vibration records by using an iterative Maximum Likelihood procedure. The procedure relies on the expectation-maximization algorithm and the Unscented Kalman filter for estimation of hidden system states. The algorithm has been used to predict the normal operating horizon of a single-stage gearbox system. Several test runs of the system have been preformed to validate the algorithm.

关键词： asset management systems condition monitoring condition monitoring condition prognosis damage progression drives expectation-maximisation algorithm expectation-maximization algorithm gearboxes gears maximum likelihood estimation maximum likelihood procedure mechanical drives nonlinear dynamical models stochastic nonlinear model unscented Kalman filter vibration vibrations wear wear

来源：评论

学校读者我要写书评

暂无评论

MODELING SPEECH PARAMETER SEQUENCES WITH LATENT TRAJECTORY HIDDEN MARKOV MODEL 25

MODELING SPEECH PARAMETER SEQUENCES WITH LATENT TRAJECTORY H...

引用

IEEE International Workshop on Machine Learning for Signal Processing

作者： Kameoka, Hirokazu NTT Corp Tokyo Japan Univ Tokyo Tokyo 1138654 Japan

ISBN: (纸本)9781467374545

This paper proposes a probabilistic generative model of a sequence of vectors called the latent trajectory hidden Markov model (HMM). While a conventional HMM is only capable of describing piecewise stationary sequences of data vectors, the proposed model is capable of describing continuously time-varying sequences of data vectors, governed by discrete hidden states. This feature is noteworthy in that it can be used to model many kinds of time series data that are continuous in nature such as speech spectra. Given a sequence of observed data, the optimal state sequence can be decoded using the expectation-maximization (EM) algorithm. Given a set of training examples, the underlying model parameters can be trained by either the expectation-maximization algorithm or the variational inference algorithm.

关键词： Sequential modeling Hidden Markov model (HMM) Trajectory HMM Latent trajectory HMM expectation-maximization algorithm variational inference

来源：评论

学校读者我要写书评

暂无评论

Model-based clustering with Hidden Markov Model regression for time series with regime changes

Model-based clustering with Hidden Markov Model regression f...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Chamroukhi, Faicel Same, Allou Aknin, Patrice Govaert, Gerard Univ Paris 13 Comp Sci Lab CNRS LIPNUMR 7030 F-93430 Villetaneuse France IFSTTAR Research Unit UPE GRETTIA Boulogne France CNRS UMR 6599 Heudiasyc Lab Paris France

ISBN: (纸本)9781424496365

This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governed by hidden Markov chains. The underlying hidden process for each cluster activates successively several polynomial regimes during time. The parameter estimation is performed by the maximum likelihood method through a dedicated expectation-maximization (EM) algorithm. The proposed approach is evaluated using simulated time series and real-world time series issued from a railway diagnosis application. Comparisons with existing approaches for time series clustering, including the stand EM for Gaussian mixtures, K-means clustering, the standard mixture of regression models and mixture of Hidden Markov Models, demonstrate the effectiveness of the proposed approach.

关键词： Clustering algorithms Data models Gaussian mixtures Hidden Markov models K-means clustering Markov processes Polynomials Spline Time series analysis expectation-maximisation algorithm expectation-maximization algorithm hidden Markov model regression hidden Markov models maximum likelihood method model-based clustering parameter estimation pattern clustering polynomial regressions railway diagnosis railway engineering regression analysis time series time series clustering

来源：评论

学校读者我要写书评

暂无评论

NON-NEGATIVE DECOMPOSITION OF LINEAR RELATIONSHIPS: APPLICATION TO MULTI-SOURCE OCEAN REMOTE SENSING DATA 41

NON-NEGATIVE DECOMPOSITION OF LINEAR RELATIONSHIPS: APPLICAT...

引用

41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Lopez-Radcenco, Manuel Aissa-El-Bey, Abdeldjalil Ailliot, Pierre Tandeo, Pierre Fablet, Ronan Telecom Bretagne Inst Mines Telecom UMR CNRS LabSTICC 6285 Technopole Brest Iroise CS83818 F-29238 Brest 3 France Univ Brest Lab Math Bretagne Atlantique UMR 6205 6 Ave Victor Le GorgeuBP 809 F-29285 Brest France

ISBN: (纸本)9781479999880

The identification and separation of contributions associated with different sources or processes is a general problem in signal and image processing. Here, we focus on the decomposition of multiple linear relationships and introduce a non-negative formulation. The proposed models can be viewed as generalizations of latent class regression models and account for possibly varying magnitudes of the linear transfer functions. Along with these models, we present model calibration algorithms. We first demonstrate their performance on simulated data. We also report an application to the analysis of upper ocean dynamics from remote sensing data (namely, satellite-derived Sea Surface Height (SSH) and Sea Surface temperature (SST) image series). This application further stresses the proposed formulation's relevance compared to state-of-the-art regression models.

关键词： Linear relationships non-negativity latent class model expectation-maximization algorithm multi-source remote sensing data

来源：评论

学校读者我要写书评

暂无评论

Recognition of hand gesture based on Gaussian Mixture Model

Recognition of hand gesture based on Gaussian Mixture Model

引用

International Workshop on Content-Based Multimedia Indexing

作者： Jia, Jia Jiang, Jianmin Wang, Dong Univ Bradford Sch Informat EIMC Bradford BD7 1DP W Yorkshire England

ISBN: (纸本)9781424420438

This paper presents, a new method for gesture recognition of Human beings' hand. This method integrates the features of shape, color and orientation histograms, which are extracted from images, and estimate the comparability with all the different types of gestures by a proposed expectation-maximization algorithm in Gaussian Mixture Model. The classification results were presented based on the values of likelihood compared with all the types of pre-assigned images, and the performance of this approach in an experiment is shown that the proposed method works well.

关键词： Gaussian processes expectation-maximisation algorithm feature extraction gesture recognition image classification image colour analysis Gaussian mixture model expectation-maximization algorithm hand gesture recognition orientation histograms preassigned Gesture recognition expectation-maximisation algorithm image colour analysis Gaussian processes Image classification Feature extraction Mixture models

来源：评论

学校读者我要写书评

暂无评论

Assessment of School Performance Through a Multilevel Latent Markov Rasch Model

引用

JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICS 2011年第4期36卷 491-522页

作者： Bartolucci, Francesco Pennoni, Fulvia Vittadini, Giorgio Univ Perugia Dept Econ Finance & Stat I-06123 Perugia Italy Univ Milano Bicocca Dept Stat I-20126 Milan Italy Univ Milano Bicocca Dept Quantitat Methods Business & Econ Sci I-20126 Milan Italy Univ Milano Bicocca Interuniv Res Ctr Publ Serv CRISP I-20126 Milan Italy

An extension of the latent Markov Rasch model is described for the analysis of binary longitudinal data with covariates when subjects are collected in clusters, such as students clustered in classes. For each subject, a latent process is used to represent the characteristic of interest (e.g., ability) conditional on the effect of the cluster to which he or she belongs. The latter effect is modeled by a discrete latent variable associated to each cluster. For the maximum likelihood estimation of the model parameters, an expectation-maximization algorithm is outlined. Through the analysis of a data set collected in the Lombardy Region (Italy), it is shown how the proposed model may be used for assessing the development of cognitive achievement. The data set is based on test scores in mathematics observed over 3 years on middle school students attending public and non-state schools.

关键词： binary longitudinal data cognitive achievement expectation-maximization algorithm school effectiveness

来源：评论

学校读者我要写书评

暂无评论

EM-based ML Channel Estimation in OFDM Systems with Phase Distortion Using RB-EKF

EM-based ML Channel Estimation in OFDM Systems with Phase Di...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：