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

317 篇 会议
176 篇 期刊文献

馆藏范围

493 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

433 篇 工学
- 226 篇 电气工程
- 154 篇 计算机科学与技术...
- 102 篇 信息与通信工程
- 51 篇 控制科学与工程
- 37 篇 机械工程
- 30 篇 软件工程
- 27 篇 电子科学与技术（可...
- 25 篇 仪器科学与技术
- 20 篇 生物医学工程（可授...
- 17 篇 交通运输工程
- 9 篇 航空宇航科学与技...
- 9 篇 核科学与技术
- 7 篇 船舶与海洋工程
- 7 篇 生物工程
- 6 篇 测绘科学与技术
- 4 篇 化学工程与技术
97 篇 理学
- 33 篇 物理学
- 26 篇 统计学（可授理学、...
- 19 篇 系统科学
- 16 篇 生物学
- 9 篇 数学
- 3 篇 化学
48 篇 医学
- 25 篇 临床医学
- 21 篇 基础医学(可授医学...
- 11 篇 特种医学
- 4 篇 公共卫生与预防医...
39 篇 管理学
- 33 篇 管理科学与工程(可...
11 篇 农学
- 8 篇 作物学
9 篇 经济学
- 7 篇 应用经济学
- 3 篇 理论经济学
1 篇 法学
1 篇 文学
1 篇 军事学

主题

493 篇 expectation-maxi...
48 篇 em algorithm
37 篇 parameter estima...
34 篇 learning (artifi...
34 篇 gaussian process...
34 篇 maximum likeliho...
30 篇 algorithms
29 篇 mixture models
28 篇 bayes methods
28 篇 channel estimati...
25 篇 expectation-maxi...
24 篇 image reconstruc...
23 篇 hidden markov mo...
22 篇 probability
22 篇 monte carlo meth...
22 篇 medical image pr...
20 篇 maximum likeliho...
19 篇 ofdm modulation
19 篇 iterative method...
18 篇 gaussian distrib...

机构

3 篇 duke univ dept e...
3 篇 univ so calif de...
3 篇 graduate school ...
2 篇 spcom group univ...
2 篇 supelec departme...
2 篇 univ victoria de...
2 篇 inria centre ren...
2 篇 princeton univer...
2 篇 harbin inst tech...
2 篇 oklahoma state u...
2 篇 spcom group univ...
2 篇 microsoft res re...
2 篇 nanjing univ inf...
2 篇 linkoping univ d...
2 篇 calif state univ...
2 篇 northwestern pol...
2 篇 army engn univ d...
2 篇 univ windsor dep...
2 篇 microsoft resear...
2 篇 bar ilan univ fa...

作者

4 篇 calinon sylvain
3 篇 bartolucci franc...
3 篇 caldwell darwin ...
3 篇 okamura hiroyuki
3 篇 yang xianqiang
3 篇 rodrigo carvajal
3 篇 dohi tadashi
3 篇 tomohiro nakatan...
3 篇 krolik jeffrey l...
3 篇 boris i. godoy
3 篇 forbes florence
2 篇 rogers jeffrey s...
2 篇 mehrpouyan hani
2 篇 juan c. agüero
2 篇 wu qing ming jon...
2 篇 a. pages-zamora
2 篇 liu xin
2 篇 moeneclaey marc
2 篇 hirokazu kameoka
2 篇 c. herzet

语言

476 篇 英文
12 篇 其他
5 篇 中文

检索条件"主题词=Expectation-maximisation algorithm"

共 493 条记录，以下是351-360 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：

Knocking Detection in Gasoline Engines Based on Probability Density Functions: A Mixed Gaussian Distribution Approach

Knocking Detection in Gasoline Engines Based on Probability ...

引用

IEEE Annual Conference on Decision and Control

作者： Tatsuya Ibuki Yasuhiro Awai Yoshihiro Sakayanagi Mitsuji Sampei Junichi Kako Department of Mechanical and Control Engineering Tokyo Institute of Technology Tokyo 152-8550 JAPAN Advanced Engine Management System Development Div. Unit Center Toyota Motor Corporation Shizuoka 410-1193 JAPAN

ISBN: (纸本)9781479978878

This paper proposes an engine knocking detection approach based on probability density functions (PDFs). In this work, we suppose that the PDF of knocking intensity distribution can be approximated by a mixture of two Gaussian functions due to normal combustion and abnormal one. We first apply EM (expectation-Maximization) algorithm to actual engine data to show that the knocking intensity probability distribution can be successfully estimated by the mixed Gaussian distribution. We next try to apply the existing online EM algorithm in view of the actual implementation by an engine control unit. However, since the existing algorithm is built for the estimation of a fixed PDF, the effect of new input data is gradually attenuated and vanishes after long time. We thus newly propose a modified online EM algorithm so that the effect of the new input data is not attenuated. Finally, we perform simulations and an actual engine bench test for the validity of the present approach.

关键词： expectation-maximisation algorithm gasoline engine Gaussian distribution incoming data Probability density function engine pedestal

来源：评论

学校读者我要写书评

暂无评论

Bin-EM-CEM algorithms of spherical parsimonious Gaussian mixture models for binned data clustering

Bin-EM-CEM algorithms of spherical parsimonious Gaussian mix...

引用

IEEE International Conference on Intelligent Engineering Systems

作者： Hani Hamdan Jingwen Wu SUPELEC Department of Signal Processing and Electronic Systems

ISBN: (纸本)9781479908271

EM algorithm is widely used in clustering domain because of its easy implementation and small storage space. CEM algorithm, which is considered as a classification version of EM algorithm, is another common used clustering algorithm. With the development of technology, we obtain more and more data. This results in slow computation of EM and CEM algorithms. Binning data seems to be efficient in gaining computation time by reducing the number of observations to the number of bins. Thus, EM and CEM algorithms applied to binned data were proposed: binned-EM and bin-EM-CEM algorithms. Moreover, fourteen parsimonious Gaussian mixture models, generated according to eigenvalue decomposition of the variance matrices of the mixture components, have less parameters than the most general model. By applying the EM and CEM algorithms of parsimonious models, estimation process is simplified and then accelerated. In this paper, to combine the advantages of binned data and parsimonious Gaussian mixture models, we develop bin-EM-CEM algorithms of spherical parsimonious Gaussian mixture models.

关键词： expectation-maximisation algorithm algorithms data clustering Sphericity Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

LEARNING IN GAUSSIAN MARKOV RANDOM FIELDS

LEARNING IN GAUSSIAN MARKOV RANDOM FIELDS

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Thomas J. Riedl Andrew C. Singer Jun Won Choi University of Illinois at Urbana-Champaign

ISBN: (纸本)9781424442959

This paper addresses the problem of state estimation in the case where the prior distribution of the states is not perfectly known but instead is parameterized by some unknown parameter. Thus in order to support the state estimator with prior information on the states and improve the quality of the state estimates, it is necessary to learn this unknown parameter first. Here we assume a parameterized Gaussian Markov random field to model the prior distribution of the states and propose an algorithm that is able to learn its parameters from given observations on these states. The effectiveness of this approach is proven experimentally by simulations.

关键词： Gaussian Markov random field EM algorithm unsupervised learning sum-product algorithm factor graph Sum-product algorithm Factor Graph State estimation Unsupervised learning Markov random fields expectation-maximisation algorithm Prior distribution Gauss Unknown parameter Learning

来源：评论

学校读者我要写书评

暂无评论

Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled

Deep Hand: How to Train a CNN on 1 Million Hand Images When ...

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Oscar Koller Hermann Ney Richard Bowden Human Language Technology & Pattern Recog. RWTH Aachen University Centre for Vision Speech & Signal Processing University of Surrey

ISBN: (纸本)9781467388528

This work presents a new approach to learning a frame-based classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos. Although we demonstrate this in the context of hand shape recognition, the approach has wider application to any video recognition task where frame level labelling is not available. The iterative EM algorithm leverages the discriminative ability of the CNN to iteratively refine the frame level annotation and subsequent training of the CNN. By embedding the classifier within an EM framework the CNN can easily be trained on 1 million hand images. We demonstrate that the final classifier generalises over both individuals and data sets. The algorithm is evaluated on over 3000 manually labelled hand shape images of 60 different classes which will be released to the community. Furthermore, we demonstrate its use in continuous sign language recognition on two publicly available large sign language data sets, where it outperforms the current state-of-the-art by a large margin. To our knowledge no previous work has explored expectation maximization without Gaussian mixture models to exploit weak sequence labels for sign language recognition.

关键词： expectation-maximisation algorithm Hand frame level sign language recognition images Classifiers

来源：评论

学校读者我要写书评

暂无评论

Anti-ship missile target tracking based on guidance law estimation

Anti-ship missile target tracking based on guidance law esti...

引用

International Conference on Information Fusion

作者： Gaoru Xue Xiaoxu Wang Wenchao Zhan Yan Liang Chunhui Zhao Ministry of Education Key Laboratory of Information Fusion Technology Xi'an Shaanxi P.R.China

A novel guidance law estimation filter (GLEF) based on expectation maximization (EM) algorithm to track anti-ship missile target with mode switching is proposed in this paper. Firstly, joint estimation and identification using EM is to minimize the parameters uncertainty and improve estimation accuracy, which is used only in the special environments in order to decrease the calculation burden. Secondly, a new filter framework just like “electric circuit fault detection” is designed to detect mode change or parameters change. Finally, anti-ship missile's threat index such as the time of flight remaining and the impact angle is estimated. The effectiveness of the proposed method and the performance of GLEF are examined through the numerical simulation.

关键词： target tracking estimation theory expectation-maximisation algorithm missile guidance missiles expectation-maximisation algorithm guided missile guidance Target tracking missiles estimation theory ANTISHIP MISSILES Impact angle parameter uncertainty State Change Defect detection

来源：评论

学校读者我要写书评

暂无评论

A Global Structural EM algorithm for a Model of Cancer Progression 11

A Global Structural EM Algorithm for a Model of Cancer Progr...

引用

Annual Conference on Neural Information Processing Systems

作者： Ali Tofigh Erik Sjoelund Mattias Hoglund Jens Lagergren School of Computer Science McGill Centre for Bioinformatics McGill University Canada Stockholm Bioinformatics Center Stockholm University Sweden Department of Oncology Lund University Sweden Center Stockholm Bioinformatics Center School of Computer Science and Communication KTH Royal Institute of Technology Sweden

ISBN: (纸本)9781618395993

Cancer has complex patterns of progression that include converging as well as diverging progressional pathways. Vogelstein's path model of colon cancer was a pioneering contribution to cancer research. Since then, several attempts have been made at obtaining mathematical models of cancer progression, devising learning algorithms, and applying these to cross-sectional data. Beerenwinkel et al. provided, what they coined, EM-like algorithms for Oncogenetic Trees (OTs) and mixtures of such. Given the small size of current and future data sets, it is important to minimize the number of parameters of a model. For this reason, we too focus on tree-based models and introduce Hidden-variable Oncogenetic Trees (HOTs). In contrast to OTs, HOTs allow for errors in the data and thereby provide more realistic modeling. We also design global structural EM algorithms for learning HOTs and mixtures of HOTs (HOT-mixtures). The algorithms are global in the sense that, during the M-step, they find a structure that yields a global maximum of the expected complete log-likelihood rather than merely one that improves it. The algorithm for single HOTs performs very well on reasonable-sized data sets, while that for HOT-mixtures requires data sets of sizes obtainable only with tomorrow's more cost-efficient technologies.

关键词： Tumor Progression expectation-maximisation algorithm cancer research algorithms Dataset Global Maximum Representation of data mixtures learning algorithms Cancer of Colon

来源：评论

学校读者我要写书评

暂无评论

Learning Temporally Consistent Rigidities

Learning Temporally Consistent Rigidities

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Jean-Sebastien Franco Edmond Boyer LJK INRIA Grenoble Rhone-Alpes

ISBN: (纸本)9781457703942

We present a novel probabilistic framework for rigid tracking and segmentation of shapes observed from multiple cameras. Most existing methods have focused on solving each of these problems individually, segmenting the shape assuming surface registration is solved, or conversely performing surface registration assuming shape segmentation or kinematic structure is known. We assume no prior kinematic or registration knowledge except for an over-estimate k of the number of rigidities in the scene, instead proposing to simultaneously discover, adapt, and track its rigid structure on the fly. We simultaneously segment and infer poses of rigid subcomponents of a single chosen reference mesh acquired in the sequence. We show that this problem can be rigorously cast as a likelihood maximization over rigid component parameters. We solve this problem using an expectation Maximization algorithm, with latent observation assignments to reference vertices and rigid parts. Our experiments on synthetic and real data show the validity of the method, robustness to noise, and its promising applicability to complex sequences.

关键词： stiffness expectation-maximisation algorithm Rigid RIGID STRUCTURES segmentation Registration Flies Solve Noise robustness kinematics

来源：评论

学校读者我要写书评

暂无评论

SCALABLE BAYESIAN NONPARAMETRIC DICTIONARY LEARNING

SCALABLE BAYESIAN NONPARAMETRIC DICTIONARY LEARNING

引用

European Signal Processing Conference

作者： Sarper Sertoglu John Paisley Department of Computer Science Columbia University Department of Electrical Engineering Columbia University

ISBN: (纸本)9781479988518

We derive a stochastic EM algorithm for scalable dictionary learning with the beta-Bernoulli process, a Bayesian nonparametric prior that learns the dictionary size in addition to the sparse coding of each signal. The core EM algorithm provides a new way for doing inference in nonparametric dictionary learning models and has a close similarity to other sparse coding methods such as K-SVD. Our stochastic extension for handling large data sets is closely related to stochastic variational inference, with the stochastic update for one parameter exactly that found using SVI. We show our algorithm compares well with K-SVD and total variation minimization on a denoising problem using several images.

关键词： expectation-maximisation algorithm Sparse coding Dictionaries as Topic one-parameter Stochastic Sludge volume index Learning Dataset Variational Inference

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach to Model Error Modelling using the expectation-Maximization algorithm

A Novel Approach to Model Error Modelling using the Expectat...

引用

IEEE Annual Conference on Decision and Control

作者： Ramon A. Delgado Graham C. Goodwin Rodrigo Carvajal Juan C. Aguero School of Electrical Engineering and Computer Science The University of Newcastle

ISBN: (纸本)9781467320658

In this paper we develop a novel approach to model error modelling. There are natural links to others recently developed ideas. However, here we make several key departures, namely (i) we focus on relative errors;(ii) we use a broad class of model error description which includes, inter alia, the earlier idea of stochastic embedding;(iii) we estimate both, the nominal model and undermodelling simultaneously using the expectation-Maximization (EM) algorithm. Simulation studies illustrate the performance of the proposed technique.

关键词： Model error modelling System identification EM algorithm expectation-maximisation algorithm System identification model error Modeling

来源：评论

学校读者我要写书评

暂无评论

Distributed AOA-based source positioning in NLOS with sensor networks

Distributed AOA-based source positioning in NLOS with sensor...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： P. Gimenez-Febrer A. Pages-Zamora S. S. Pereira R. Lopez-Valcarce SPCOM Group Univ. Politec. de Catalunya-Barcelona Tech Barcelona Spain

ISBN: (纸本)9781467369985

This paper focuses on the problem of positioning a source using angle-of-arrival measurements taken by a wireless sensor network in which some of the nodes experience non line-of-sight (LOS) propagation conditions. In order to mitigate the errors induced by the nodes in NLOS, we derive an algorithm that combines the expectation-maximization algorithm with a weighted least-squares estimation of the source position so that the nodes in NLOS are eventually identified and discarded. Moreover, a distributed version of this algorithm based on a diffusion strategy that iteratively refines the position estimate while driving the network to a consensus is presented.

关键词： AOA NLOS distributed estimation positioning sensor networks Nonlinear optics ANGLE OF ATTACK Sensor networks Patient Positioning Ganglia position estimation Wireless sensor networks expectation-maximisation 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50页 << < 32 33 34 35 36 37 38 39 40 41 > >>

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：