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检索条件"主题词=EM Algorithm"
4573 条 记 录,以下是31-40 订阅
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Network Traffic Identification Based on Improved em algorithm
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IEEE ACCESS 2024年 12卷 26773-26786页
作者: Cui, Hao Liang, Linjie Wang, Jinghui Jilin Univ Architecture & Technol Network Ctr Changchun 130114 Peoples R China Jilin Univ Architecture & Technol Sch Civil Engn Changchun 130114 Peoples R China Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China
With the continuous increase and complexity of network traffic, traditional network traffic recognition technology is facing numerous difficulties, especially in dealing with outlier data and improving recognition acc... 详细信息
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Robust Model-Based Learning via Spatial-em algorithm
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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2015年 第6期27卷 1670-1682页
作者: Yu, Kai Dang, Xin Bart, Henry, Jr. Chen, Yixin Amazon Web Serv Seattle WA 98101 USA Univ Mississippi Dept Math University MS 38677 USA Tulane Univ Dept Ecol & Evolutionary Biol New Orleans LA 70118 USA Univ Mississippi Dept Comp & Informat Sci University MS 38677 USA
This paper presents a new robust em algorithm for the finite mixture learning procedures. The proposed Spatial-em algorithm utilizes median-based location and rank-based scatter estimators to replace sample mean and s... 详细信息
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Mixture of robust Gaussian processes and its hard-cut em algorithm with variational bounding approximation
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NEUROCOMPUTING 2021年 452卷 224-238页
作者: Li, Tao Wu, Di Ma, Jinwen Peking Univ Sch Math Sci Dept Informat Sci Beijing 100871 Peoples R China Peking Univ LMAM Beijing 100871 Peoples R China Shanxi Normal Univ Sch Comp Sci Xian 710100 Peoples R China
The Gaussian process is a powerful statistical learning model and has been applied widely in nonlinear regression and classification. However, it fails to model multi-modal data from a non-stationary source since a pr... 详细信息
来源: 评论
Finite mixture distributions, sequential likelihood and the em algorithm
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ECONOMETRICA 2003年 第3期71卷 933-946页
作者: Arcidiacono, P Jones, JB Duke Univ Dept Econ Durham NC 27708 USA SUNY Albany Dept Econ Albany NY 12222 USA
A popular way to account for unobserved heterogeneity is to assume that the data are drawn from a finite mixture distribution. A barrier to using finite mixture models is that parameters that could previously be estim... 详细信息
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A fast em algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits
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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2015年 85卷 37-53页
作者: Bernhardt, Paul W. Zhang, Daowen Wang, Huixia Judy Villanova Univ Dept Math & Stat Villanova PA 19085 USA N Carolina State Univ Dept Stat Raleigh NC 27695 USA George Washington Univ Dept Stat Washington DC 20052 USA
Joint modeling techniques have become a popular strategy for studying the association between a response and one or more longitudinal covariates. Motivated by the GenIMS study, where it is of interest to model the eve... 详细信息
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General em algorithm for fitting non-monotone hazard functions from truncated and censored observations
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OPERATIONS RESEARCH LETTERS 2022年 第5期50卷 476-483页
作者: Barde, Stephane Ko, Young Myoung Shin, Hayong Samsung Elect Innovat Ctr Hwasung 18448 Gyeonggi South Korea Korea Adv Inst Sci & Technol Dept Ind & Syst Engn 291 Daehak Ro Daejeon 34141 South Korea Pohang Univ Sci & Technol Dept Ind & Management Engn Pohang 37673 Gyeongbuk South Korea
Recently, many researchers focused on modeling non-monotonic hazard functions such as bath-tube and hump shapes. However, most of their estimation methods are focused on complete observations. Since reliability data a... 详细信息
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Gaussian mean-shift is an em algorithm
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2007年 第5期29卷 767-776页
作者: Carreira-Perpinan, Miguel A. Oregon Hlth & Sci Univ Dept Comp Sci & Elect Engn OGI Sch Sci & Engn Beaverton OR 97006 USA
The mean-shift algorithm, based on ideas proposed by Fukunaga and Hostetler [ 16], is a hill-climbing algorithm on the density defined by a finite mixture or a kernel density estimate. Mean-shift can be used as a nonp... 详细信息
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The em algorithm for kernel matrix completion with auxiliary data
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JOURNAL OF MACHINE LEARNING RESEARCH 2004年 第1期4卷 67-81页
作者: Tsuda, K Akaho, S Asai, K Max Planck Inst Biol Cybernet D-72076 Tubingen Germany AIST Computat Biol Res Ctr Tokyo 1350064 Japan AIST Neurosci Res Inst Tsukuba Ibaraki 3058568 Japan Univ Tokyo Grad Sch Frontier Sci Dept Computat Biol Kashiwa Chiba 2778562 Japan
In biological data, it is often the case that observed data are available only for a subset of samples. When a kernel matrix is derived from such data, we have to leave the entries for unavailable samples as missing. ... 详细信息
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Linear Quantile Regression Based on em algorithm
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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2014年 第16期43卷 3464-3484页
作者: Tian, Yuzhu Tian, Maozai Zhu, Qianqian Henan Polytech Univ Sch Math & Informat Sci Jiaozuo 454000 Henan Province Peoples R China Renmin Univ China Ctr Appl Stat Sch Stat Beijing Peoples R China
This article aims to put forward a new method to solve the linear quantile regression problems based on em algorithm using a location-scale mixture of the asymmetric Laplace error distribution. A closed form of the es... 详细信息
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An em algorithm for the proportional hazards model with doubly censored data
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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2013年 第1期57卷 41-51页
作者: Kim, Yongdai Kim, Joungyoun Jang, Woncheol Seoul Natl Univ Dept Stat Seoul South Korea
In this paper, we consider a new procedure for estimating parameters in the proportional hazards model with doubly censored data. Computing the maximum likelihood estimator with doubly censored data is often nontrivia... 详细信息
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