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检索条件"主题词=Expectation-Maximization algorithms"
270 条 记 录,以下是21-30 订阅
排序:
Adjacency-based regularization for partially ranked data with non-ignorable missing
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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2020年 第0期145卷 106905-000页
作者: Nakamura, Kento Yano, Keisuke Komaki, Fumiyasu Univ Tokyo Grad Sch Informat Sci & Technol Dept Math Informat Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan RIKEN Ctr Brain Sci 2-1 Hirosawa Wako Saitama 3510198 Japan
In analyzing ranked data, we often encounter situations in which data are partially ranked. Regarding partially ranked data as missing data, this paper addresses parameter estimation for partially ranked data under a ... 详细信息
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An Accurate, Fast Approximation for the Sum of Fading Random Variables via expectation maximization Applications to Diversity Systems
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IEEE ACCESS 2018年 6卷 42616-42630页
作者: Vega Sanchez, Jose David Urquiza-Aguiar, Luis Paredes Paredes, Martha Cecilia Escuela Politec Nacl Dept Elect Telecomunicac & Redes Informac Fac Elect & Elect Quito 170525 Ecuador
Sums of fading envelopes occur in several wireless communications applications. The exact mathematical solution to this statistic is, however, rather intricate. In this paper, we derive a novel closed-form approximati... 详细信息
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Parameter Estimation For Bursty-Intermittent Observations
Parameter Estimation For Bursty-Intermittent Observations
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IEEE Signal Processing and Communications Applications (SIU)
作者: Çagatay Candan Elektrik ve Elektronik Mühendisliği Bölümü Orta Doğu Teknik Üniversitesi
Parameter estimation problem is examined in the setting where the noise power is allowed to change from sample to sample. Parameters of the noise source is assumed to be generated by a Markov chain whose state sequenc... 详细信息
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Solving Time-Domain Audio Inverse Problem Using Nonnegative Tensor Factorization
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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年 第21期66卷 5604-5617页
作者: Bilen, Cagdas Ozerov, Alexey Perez, Patrick Audio Analyt Ltd Cambridge CB5 8AB England Technicolor F-35576 Cesson Sevigne France Valeo Ai F-75017 Paris France
Nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) are important tools for modeling nonnegative data, which gained increasing popularity in various fields, a significant one of which is ... 详细信息
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A GAMP-Based Low Complexity Sparse Bayesian Learning Algorithm
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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年 第2期66卷 294-308页
作者: Al-Shoukairi, Maher Schniter, Philip Rao, Bhaskar D. Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92093 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA
In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into expectation-maximization-based sparse Bayesian learn... 详细信息
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Mixture Semisupervised Bayesian Principal Component Regression for Soft Sensor Modeling
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IEEE ACCESS 2018年 6卷 40909-40919页
作者: Zhu, Pengbo Liu, Xin Wang, Yanbo Yang, Xianqiang Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Heilongjiang Peoples R China
In this paper, a mixture semisupervised Bayesian principal component regression-based soft sensor modeling method for nonlinear industrial process with multiple operating modes is presented. In many chemistry processe... 详细信息
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A Framework for Clustered and Skewed Sparse Signal Recovery
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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年 第15期66卷 3972-3986页
作者: Wang, Sheng Rahnavard, Nazanin Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA
A novel framework, clustered-skew normal mixture-belief propagation, is developed to solve the reconstruction of undersampled clustered signals, where the magnitudes of signal coefficients in each cluster are distribu... 详细信息
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ON THE IMPLEMENTATION AND ANALYSIS OF expectation maximization algorithms WITH STOPPING CRITERIONmaximization
ON THE IMPLEMENTATION AND ANALYSIS OF EXPECTATION MAXIMIZATI...
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19th IEEE International Conference on Image Processing (ICIP)
作者: Ryan, Andrew Mora, Benjamin Chen, Min Swansea Univ Coll Sci Dept Comp Sci Swansea W Glam Wales Univ Oxford Oxford E Res Ctr Oxford England
The expectation maximization (EM) algorithm is an alternative reconstruction method to the Filtered Back Projection method, providing many advantages including decreased sensitivity to noise. However the algorithm req... 详细信息
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Performance Comparison of Recent Imputation Methods for Classification Tasks over Binary Data
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APPLIED ARTIFICIAL INTELLIGENCE 2017年 第1期31卷 1-22页
作者: Ghorbani, Soroosh Desmarais, Michel C. Polytech Montreal Dept Comp Engn 2900 Boul Edouard Montpetit Montreal PQ H3T 1J4 Canada
This paper evaluates the effect on the predictive accuracy of different models of two recently proposed imputation methods, namely missForest (MF) and Multiple Imputation based on expectation-maximization (MIEM), alon... 详细信息
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Stochastic degradation process modeling and remaining useful life estimation with flexible random-effects
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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2017年 第6期354卷 2477-2499页
作者: Zhang, Zhengxin Hu, Changhua Si, Xiaosheng Zhang, Jianxun Zheng, Jianfei Xian Inst High Tech Xian 710025 Shaanxi Provinc Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China
Models with random-effects are generally used in the field of degradation modeling and remaining useful life (RUL) estimation for describing unit-to-unit variability. The wide employment of parameters, which is assume... 详细信息
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