检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

444 篇 期刊文献
205 篇 会议
1 篇 学位论文
1 篇 资讯

馆藏范围

651 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

472 篇 工学
- 261 篇 计算机科学与技术...
- 146 篇 软件工程
- 131 篇 电气工程
- 89 篇 信息与通信工程
- 58 篇 控制科学与工程
- 20 篇 仪器科学与技术
- 20 篇 生物工程
- 18 篇 安全科学与工程
- 17 篇 生物医学工程（可授...
- 16 篇 机械工程
- 16 篇 电子科学与技术（可...
- 12 篇 土木工程
- 12 篇 化学工程与技术
- 9 篇 光学工程
442 篇 理学
- 355 篇 数学
- 347 篇 统计学（可授理学、...
- 49 篇 生物学
- 38 篇 物理学
- 25 篇 系统科学
- 11 篇 化学
- 8 篇 地球物理学
67 篇 管理学
- 40 篇 管理科学与工程(可...
- 15 篇 工商管理
- 15 篇 图书情报与档案管...
- 9 篇 公共管理
50 篇 医学
- 26 篇 临床医学
- 17 篇 基础医学(可授医学...
- 8 篇 公共卫生与预防医...
25 篇 经济学
- 21 篇 应用经济学
15 篇 农学
8 篇 法学
- 8 篇 社会学
4 篇 教育学
3 篇 文学

主题

651 篇 expectation maxi...
20 篇 maximum likeliho...
16 篇 gaussian mixture...
15 篇 parameter estima...
14 篇 expectation-maxi...
13 篇 hidden markov mo...
11 篇 maximum likeliho...
9 篇 system identific...
9 篇 image segmentati...
9 篇 kalman filter
7 篇 mixture models
7 篇 missing data
6 篇 hidden markov mo...
6 篇 channel estimati...
5 篇 multiple models
5 篇 clustering
5 篇 bayesian informa...
5 篇 expectation maxi...
5 篇 em algorithm
4 篇 maximum likeliho...

机构

4 篇 institute of aco...
3 篇 univ alberta dep...
3 篇 imt school for a...
3 篇 school of mathem...
3 篇 school of automa...
3 篇 univ melbourne d...
2 篇 department of au...
2 篇 tsinghua univers...
2 篇 mcmaster univ de...
2 篇 zhongtai securit...
2 篇 zhejiang univ co...
2 篇 northeastern uni...
2 篇 univ so calif in...
2 篇 univ atacama fac...
2 篇 department of ma...
2 篇 vinai research v...
2 篇 jiangnan univ sc...
2 篇 center for machi...
2 篇 univ so calif de...
2 篇 department of ch...

作者

7 篇 huang biao
6 篇 krishnamurthy v
6 篇 orlando danilo
5 篇 hao chengpeng
5 篇 shlezinger nir
5 篇 elliott rj
4 篇 yan linjie
4 篇 doerschuk peter ...
4 篇 schmid luca
4 篇 schmalen laurent
4 篇 raviv tomer
3 篇 osoba osonde
3 篇 kim inyoung
3 篇 yu chengpu
3 篇 paliwal kk
3 篇 ge zhiqiang
3 篇 si xiaosheng
3 篇 pal suvra
3 篇 lei chen
3 篇 kosko bart

语言

538 篇 英文
107 篇 其他
5 篇 中文
1 篇 日文

检索条件"主题词=Expectation Maximization Algorithm"

共 651 条记录，以下是401-410 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Statistical inference for the generalized inverse exponential distribution under progressive first failure censoring scheme

Statistical inference for the generalized inverse exponentia...

引用

2024 International Conference on algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Li, Zhaohui Faculty of Social Sciences University of Southampton Southampton United Kingdom

ISBN: (纸本)9781510685840

This article considers the point estimations and interval estimations for a generally inverse exponential distribution on the basis of the progressive first failure censoring. We derive the maximum likelihood estimators along with corresponding asymptotic confidence interval at first. Next, we compute the maximum likelihood through the utilization of the expectation maximization (EM) algorithm. Afterwards, Bayesian inference is applied using both symmetric and asymmetric loss functions in scenarios involving either informative or non-informative priors. The M-H algorithm is utilized to compute point estimation, which leads to the highest posterior density credible intervals simultaneously. Eventually, a numerical simulation test is executed for the sake of evaluating the qualities of estimations mentioned and authentic dataset is investigated. © 2024 SPIE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Residual Neural Networks for the Prediction of the Regularization Parameters in PET Reconstruction 46

Residual Neural Networks for the Prediction of the Regulariz...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Moussaoui, Younes Mateus, Diana Moussaoui, Saïd Carlier, Thomas Stute, Simon Nantes Université École Centrale Nantes LS2N Cnrs Umr 6004 NantesF-44300 France Chu Nantes Service de Médecine Nucléaire NantesF-44000 France Nantes Université CRCI2NA NantesF-44000 France

ISBN: (纸本)9798350371499

Positron Emission Tomography (PET) is a medical imaging modality relying on numerical methods that integrate the statistical properties of the measurements and prior assumptions about the images. In order to maximize the computed image quality, PET reconstruction algorithms require the setting of hyperparameters that balance data fidelity with regularization. However, their optimal tuning depends on the statistical properties of the raw data and on the clinical objectives. To address this issue, we propose a supervised deep learning strategy based on a residual neural network that takes the raw measured data (sinogram) as input and automatically predicts the optimal value of the regularization parameter of the modified block Sequential Regularized expectation maximization (BSREM) algorithm. The proposed strategy is trained on a synthetic dataset consisting of 2D sinograms and their corresponding optimal regularization parameters. Our results demonstrate the feasibility of the approach leading to improved image reconstruction compared to classical manual tuning methods. © 2024 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Multivariate Bounded Support Kotz Mixture Model with Minimum Message Length Criterion

Multivariate Bounded Support Kotz Mixture Model with Minimum...

引用

2024 International Symposium on Networks, Computers and Communications, ISNCC 2024

作者： Araya, Tsega Weldu Azam, Muhammad Bouguila, Nizar Bentahar, Jamal Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada School of Computer Science and Technology Algoma University Sault Ste. MarieON Canada

ISBN: (纸本)9798350364910

In this paper, we present a multivariate bounded Kotz mixture model (BKMM) for data modeling when the data lies in a bounded support region. In BKMM, parameter estimation is performed by maximizing the log-likelihood through expectation-maximization (EM). Model selection in mixtures is considered essential for determining the optimal number of mixture components. Thus, we propose a model selection criterion for BKMM using minimum message length (MML). Initially, we validate the proposed model selection criteria for identifying the exact number of components using five medical disease diagnosis data. Additionally, to further assess its performance, we examined the model using various image datasets such as Alzheimer, lung tissue, gastrointestinal tract and object categorization. The results of MML are compared with seven different model selection criteria to examine the effectiveness of the proposed model. The experimental results demonstrate the effectiveness of the BKMM and MML. © 2024 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Achieving Margin maximization Exponentially Fast via Progressive Norm Rescaling 41

Achieving Margin Maximization Exponentially Fast via Progres...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Wang, Mingze Min, Zeping Wu, Lei School of Mathematical Sciences Peking University Beijing China Center for Machine Learning Research Peking University Beijing China

In this work, we investigate the margin-maximization bias exhibited by gradient-based algorithms in classifying linearly separable data. We present an in-depth analysis of the specific properties of the velocity field associated with (normalized) gradients, focusing on their role in margin maximization. Inspired by this analysis, we propose a novel algorithm called Progressive Rescaling Gradient Descent (PRGD) and show that PRGD can maximize the margin at an exponential rate. This stands in stark contrast to all existing algorithms, which maximize the margin at a slow polynomial rate. Specifically, we identify mild conditions on data distribution under which existing algorithms such as gradient descent (GD) and normalized gradient descent (NGD) provably fail in maximizing the margin efficiently. To validate our theoretical findings, we present both synthetic and real-world experiments. Notably, PRGD also shows promise in enhancing the generalization performance when applied to linearly non-separable datasets and deep neural networks. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Efficient Dual-Confounding Eliminating for Weakly-supervised Temporal Action Localization 24

Efficient Dual-Confounding Eliminating for Weakly-supervised...

引用

32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Ao Liu, Huijun Sheng, Jinrong Chen, Zhongming Ge, Yongxin School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798400706868

Weakly-supervised Temporal Action Localization (WTAL) following a localization-by-classification paradigm has achieved significant results, yet still grapples with confounding arising from ambiguous snippets. Previous works have attempted to distinguish these ambiguous snippets from action snippets without investigating the underlying causes of their formation, thus failing to effectively eliminate the bias on both action-context and action-content. In this paper, we revisit WTAL from the perspective of structural causal model to identify the true origins of confounding, and propose an efficient dual-confounding eliminating framework to alleviate these biases. Specifically, we construct a Substituted Confounder Set (SCS) to eliminate the confounding bias on action-context by leveraging the modal disparity between RGB and FLOW. Then, a Multi-level Consistency Mining (MCM) method is designed to mitigate the confounding bias on action-content by utilizing the consistency between discriminative snippets and corresponding proposals at both the feature and label levels. Notably, SCS and MCM could be seamlessly integrated into any two-stream models without additional parameters by expectation-maximization (EM) algorithm. Extensive experiments on two challenging benchmarks including THUMOS14 and ActivityNet-1.2 demonstrate the superior performance of our method. © 2024 ACM.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent Trajectory Planning with NUV Priors

Multi-Agent Trajectory Planning with NUV Priors

引用

2024 American Control Conference, ACC 2024

作者： Van Erp, Bart Bagaev, Dmitry Podusenko, Albert Senoz, Ismail De Vries, Bert Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands Lazy Dynamics Eindhoven Netherlands GN Hearing Eindhoven Netherlands

ISBN: (纸本)9798350382655

This paper presents a probabilistic model-based approach to centralized multi-agent trajectory planning. This approach allows for incorporating uncertainty of the state and dynamics of the agents directly in the model. Probabilistic inference is then efficiently automated using message passing. The recently introduced normal-with-unknown-variance (NUV) priors are used to prevent collisions between agents and obstacles. Furthermore, a new expectation-maximization inference scheme is derived for box and half-space NUV priors, which takes state uncertainty into account when avoiding collisions. © 2024 AACC.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhanced Channel Estimation for Multi-tag Ambient Backscatter Communication Systems 7

Enhanced Channel Estimation for Multi-tag Ambient Backscatte...

引用

7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Abdallah, Saeed Saad, Mohamed Albreem, Mahmoud University of Sharjah Dept. of Electrical Engineering Sharjah United Arab Emirates University of Sharjah Dept. of Computer Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350368673

Ambient backscatter communication (AmBC) is a highly promising communication paradigm for the next generation, energy efficient Internet-of-Things (IoT) applications. In this work, we consider the problem of channel estimation for multi-tag AmBC systems. Recent works have proposed pilot-based solutions for this problem, which incur a significant training overhead. In this work, we propose a novel semi-blind algorithm for channel estimation in multi-tag AmBCs. The proposed algorithm is based on the expectation maximization framework and incorporates both pilot sequences and data symbols into the estimation process. Our simulations demonstrate the mean-squared error performance of the proposed scheme, showing that it yields substantially higher estimation accuracy than the state of the art. © 2024 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimality Gap of Decentralized Submodular maximization under Probabilistic Communication 63

Optimality Gap of Decentralized Submodular Maximization unde...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Vendrell, Joan Kia, Solmaz University of California Irvie Engineering Department IrvineCA United States

ISBN: (纸本)9798350316339

This paper considers the problem of decentralized submodular maximization subject to partition matroid constraint using a sequential greedy algorithm with probabilistic inter-agent message-passing. We propose a communication-aware framework where the probability of successful communication between connected devices is considered. Our analysis introduces the notion of the probabilistic optimality gap, highlighting its potential influence on determining the message-passing sequence based on the agent's broadcast reliability and strategic decisions regarding agents that can broadcast their messages multiple times in a resource-limited environment. This work not only contributes theoretical insights but also has practical implications for designing and analyzing decentralized systems in uncertain communication environments. A numerical example demonstrates the impact of our results. © 2024 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Joint learning of Gaussian graphical models in heterogeneous dependencies of high-dimensional transcriptomic data 16

Joint learning of Gaussian graphical models in heterogeneous...

引用

16th Asian Conference on Machine Learning, ACML 2024

作者： Nguyen, Dung Ngoc Li, Zitong CSIRO Agriculture and Food CanberraACT Australia

In biology, constructing gene co-expression networks presents a significant research challenge, largely due to the high dimensionality of the data and the heterogeneity of the samples. Furthermore, observations from two or more groups sharing the same biological variables require the comparison of gene co-expression patterns with some commonalities between the groups. In this context, we propose a mixture of Gaussian graphical models for paired data to estimate heterogeneous dependencies and uncover sub-population networks within complex biological datasets, incorporating sparsity and symmetry constraints between two groups of dependent variables. We develop an efficient generalized expectation-maximization (EM) algorithm for penalized maximum likelihood estimation with the fusion of a graphical lasso penalty. As a result, our simulation studies highlight the numerical performance of the proposed method, demonstrating its superior model fitting compared to the classical graphical lasso approach. We further demonstrate the practical application of our approach by estimating gene networks on a high-dimensional ecological transcriptomics data set of the nine-spined stickleback. Our new approach identified similarities and differences between groups of genes from the brain and liver tissues of samples collected from two habitats. These results show the efficiency of our approach to the identification of complicated interactions from high-dimensional and heterogeneous gene expression data. © 2024 D.N. Nguyen & Z. Li.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

EM-based Batch algorithm for Trajectory Estimation of Multiple Targets 2

EM-based Batch Algorithm for Trajectory Estimation of Multip...

引用

2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yan, Linjie Orlando, Danilo Addabbo, Pia Fiscante, Nicomino Hao, Chengpeng Giunta, Gaetano Institute of Acoustics Chinese Academy of Sciences Beijing China Università Di Pisa Rome Italy Università Degli Studi 'Giustino Fortunato' Benevento Italy University of Roma Tre Rome Italy

ISBN: (纸本)9798331515669

This study investigates the trajectory clustering problem in the presence of multiple moving targets which are approximated by straight motion over the observation interval and monitored by a radar sensor network. The assumption is that the nodes in the network lack the knowledge of the measurements' association with each target, causing challenging estimation and classification tasks. To solve this problem, a novel unsupervised classifier based on expectation-maximization algorithm is devised by exploiting the latent variable model combined with cyclic estimation methods to obtain closed-form updates for the unknown parameters and build up a maximum a posteriori rule allowing for target measurement clustering. Finally, we analyze the effectiveness of the proposed algorithm through simulation experiments in a scenario with trajectory intersections. © 2024 IEEE.

关键词： expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共66页 << < 37 38 39 40 41 42 43 44 45 46 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：