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

261 篇 期刊文献
168 篇 会议
6 篇 学位论文

馆藏范围

435 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

394 篇 工学
- 311 篇 计算机科学与技术...
- 100 篇 电气工程
- 54 篇 控制科学与工程
- 36 篇 软件工程
- 29 篇 信息与通信工程
- 27 篇 机械工程
- 7 篇 电子科学与技术（可...
- 4 篇 石油与天然气工程
- 4 篇 交通运输工程
- 3 篇 仪器科学与技术
- 3 篇 材料科学与工程（可...
- 3 篇 土木工程
- 2 篇 力学（可授工学、理...
- 2 篇 光学工程
- 2 篇 化学工程与技术
- 2 篇 安全科学与工程
113 篇 管理学
- 113 篇 管理科学与工程(可...
- 8 篇 工商管理
- 2 篇 公共管理
62 篇 理学
- 37 篇 数学
- 17 篇 生物学
- 5 篇 物理学
- 3 篇 化学
10 篇 经济学
- 9 篇 应用经济学
- 1 篇 理论经济学
6 篇 医学
- 3 篇 基础医学(可授医学...
- 3 篇 临床医学
3 篇 农学
- 2 篇 作物学
2 篇 法学
- 1 篇 法学
- 1 篇 社会学

主题

435 篇 estimation of di...
25 篇 probability mode...
25 篇 optimization
25 篇 evolutionary alg...
23 篇 genetic algorith...
21 篇 multi-objective ...
17 篇 differential evo...
15 篇 evolutionary com...
13 篇 multiobjective o...
12 篇 eda
11 篇 local search
11 篇 bayesian network
9 篇 genetic algorith...
8 篇 probabilistic mo...
8 篇 makespan
8 篇 global optimizat...
8 篇 scheduling
7 篇 hybrid algorithm
7 篇 restricted boltz...
6 篇 genetic programm...

机构

15 篇 tsinghua univ de...
9 篇 tsinghua univ de...
8 篇 kunming univ sci...
7 篇 natl univ singap...
6 篇 kunming univ sci...
4 篇 kunming univ sci...
4 篇 tsinghua univ de...
4 篇 lanzhou univ tec...
3 篇 univ sci & techn...
3 篇 beijing inst tec...
3 篇 univ essex dept ...
3 篇 univ birmingham ...
3 篇 wuhan univ state...
3 篇 china univ min &...
3 篇 univ hyderabad s...
3 篇 liaocheng univ s...
3 篇 univ politecn ma...
3 篇 shandong normal ...
3 篇 natl chiao tung ...
3 篇 lanzhou univ tec...

作者

36 篇 wang ling
15 篇 santana roberto
12 篇 xu ye
11 篇 larranaga pedro
10 篇 bielza concha
9 篇 hu rong
9 篇 liu min
9 篇 qian bin
9 篇 wang shengyao
8 篇 lozano jose a.
7 篇 zhao fuqing
7 篇 zhang zi-qi
7 篇 mendiburu alexan...
7 篇 hernandez-aguirr...
7 篇 zhou aimin
6 篇 perez-rodriguez ...
5 篇 dozier gerry
5 篇 chaurasia sachch...
5 篇 pan quan-ke
5 篇 wang sheng-yao

语言

411 篇 英文
19 篇 其他
3 篇 中文
2 篇 西班牙文
1 篇 德文
1 篇 法文

检索条件"主题词=estimation of distribution algorithm"

共 435 条记录，以下是341-350 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：

A COMPARATIVE STUDY ON LARGE-SIZE VIDEO INDEXING 4

A COMPARATIVE STUDY ON LARGE-SIZE VIDEO INDEXING

引用

4th International Conference on Smart and Sustainable City (ICSSC)

作者： Luo, Ziyue Yu, Xiaoqing Zhong, Linxia Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Shanghai Univ Inst Smart City Shanghai 200444 Peoples R China

ISBN: (纸本)9781785615030

Video plays an important role in our daily life. But in most video websites such as YouTube, it is always a problem to classify millions of videos that are updated every day. So there is an urgent need to develop a classification algorithm to accurately assign labels to those videos. In this paper, we use Google Cloud Platform as our calculating environment and choose the new and improved YT-8M V2 as dataset. Based on these, we compare the estimation of distribution algorithm and the recurrent neural network algorithm, trace their accuracy, and finally find the more suitable one for this problem.

关键词： Video classification google cloud estimation of distribution algorithm recurrent neural network

来源：评论

学校读者我要写书评

暂无评论

Double-Objective Optimal Control for Non-Gaussian Systems: An Example Study on Analytical vs Numerical Solutions 9

Double-Objective Optimal Control for Non-Gaussian Systems: A...

引用

9th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2015

作者： Ren, Mifeng Zhang, Jianhua Wang, Hong Huang, Min Taiyuan Univ Technol Coll Informat Engn Taiyuan 030024 Peoples R China North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Univ Manchester Sch Elect & Elect Engn Control Syst Ctr Manchester M60 1QD Lancs England Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China

Minimum entropy control has been proven to be an effective method in control of non Gaussian stochastic systems. In this case, the entropy is proposed as a generalization of the variance measure to characterize the randomness of the process. Minimum entropy corresponds to small uncertainty (or derivation), but it cannot guarantee the tracking error approaching to zero. Therefore, mean square error also should be added in the criterion. In this paper, by using a simple example, the method of generating a representative approximation of the Pareto optimal control set is investigated in both analytical and numerical ways. And simulation results show the feasibility of the proposed double objective optimal control method. (C) 2015, IFAC (International Federation or Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Double-objective optimal control non-Gaussian stochastic systems Renyi's entropy epsilon - constrained method estimation of distribution algorithm

来源：评论

学校读者我要写书评

暂无评论

A Novel Multi-objective Optimization-based Image Registration Method 16

A Novel Multi-objective Optimization-based Image Registratio...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Shi, Meifeng He, Zhongshi Chen, Ziyu Zhang, Hang Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (纸本)9781450342063

The RANSAC is widely used in image registration algorithms. However, the RANSAC becomes computationally expensive when the number of feature points is large. And also, its high error matching ratio caused by the large number of iterations always raises the possibility of false registration. To deal with these drawbacks, a novel multi-objective optimization-based image registration method is proposed, named MO-IRM. In MO-IRM, a multi-objective estimation model is built to describe the feature matching pairs (data set), with no need for the pre-check process that is necessary in some improved RANSAC algorithms to eliminate the error-matching pairs. Moreover, a full variate Gaussian model-based RM-MEDA without clustering process (FRM-MEDA) is presented to solve the established multi-objective model. FRM-MEDA only requires a few iterations to find out a correct model. FRM-MEDA can not only greatly reduce the computational overhead but also effectively decrease the possibility of false registration. The proposed MO-IRM is compared with RM-MEDA, NSGA-square and the RANSAC based registration algorithm on the Dazu grottoes image database. The experiment results demonstrate that the proposed method achieves ideal registration performances on both two images and multiple images, and greatly outperforms the compared algorithms on the runtime.

关键词： Multi-objective Optimization Full variate Gaussian Model estimation of distribution algorithm Image Registration RANSAC

来源：评论

学校读者我要写书评

暂无评论

Can Compact Optimisation algorithms Be Structurally Biased? 16th

Can Compact Optimisation Algorithms Be Structurally Biased?

引用

16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Kononova, Anna, V Caraffini, Fabio Wang, Hao Back, Thomas Leiden Univ Leiden Inst Adv Comp Sci LIACS Leiden Netherlands De Montfort Univ Inst Artificial Intelligence Leicester Leics England Sorbonne Univ Paris LIP6 Paris France

ISBN: (纸本)9783030581114;9783030581121

In the field of stochastic optimisation, the so-called structural bias constitutes an undesired behaviour of an algorithm that is unable to explore the search space to a uniform extent. In this paper, we investigate whether algorithms from a subclass of estimation of distribution algorithms, the compact algorithms, exhibit structural bias. Our approach, justified in our earlier publications, is based on conducting experiments on a test function whose values are uniformly distributed in its domain. For the experiment, 81 combinations of compact algorithms and strategies of dealing with infeasible solutions have been selected as test cases. We have applied two approaches for determining the presence and severity of structural bias, namely an (existing) visual and an (updated) statistical (Anderson-Darling) test. Our results suggest that compact algorithms are more immune to structural bias than their counterparts maintaining explicit populations. Both tests indicate that strong structural bias is found only in the cBFO algorithm, regardless of the choice of strategy of dealing with infeasible solutions, and cPSO with mirror strategy. For other test cases, statistical and visual tests disagree on some cases classified as having mild or strong structural bias: the former one tends to make harsher decisions, thus needing further investigation.

关键词： Structural bias Compact algorithm Continuous optimisation estimation of distribution algorithm Infeasible solution

来源：评论

学校读者我要写书评

暂无评论

Voronoi Tessellation Optimization A Local/Global Optimization Hybrid for Electromagnetics Design

Voronoi Tessellation Optimization A Local/Global Optimizatio...

引用

USNC-URSI Radio Science Meeting / IEEE International Symposium on Antennas and Propagation (AP-S)

作者： Jenkins, Ronald P. Werner, Douglas H. Penn State Univ Dept Elect Engn & Comp Sci University Pk PA 16802 USA

ISBN: (纸本)9781728106922

This paper introduces a new hybrid local-global optimizer called the Voronoi Tessellation Optimization (VTO) algorithm which leverages precomputed gradients and a space partitioning technique to improve single objective convergence speed and robustness. VTO has been herein compared with other well-known optimization algorithms used in electromagnetics and optics and has demonstrated commensurate or improved performance on two multimodal test problems.

关键词： Optimization Voronoi Tessellation Partition Evolutionary algorithm estimation of distribution algorithm

来源：评论

学校读者我要写书评

暂无评论

Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models

Using Probabilistic Dependencies Improves the Search of Cond...

引用

8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

作者： Santana, Roberto Bielza, Concha Larranaga, Pedro Univ Politecn Madrid Dept Artificial Intelligence E-28660 Madrid Spain

ISBN: (纸本)9783642122101

Conductance-based compartmental neuron models are traditionally used to investigate the electrophysiological properties of neurons. These models require a number of parameters to be adjusted to biological experimental data and this question can be posed as an optimization problem. In this paper we investigate the behavior of different estimation of distribution algorithms (EDAs) for this problem. We focus on studying the influence that ate interactions between the neuron model conductances have in the complexity of the optimization problem. We support evidence that the use of these interactions during the optimization process can improve the EDA behavior.

关键词： Conductance-based compartmental neuron models estimation of distribution algorithm probabilistic models

来源：评论

学校读者我要写书评

暂无评论

Population-Based Incremental Learning for Effective IoT Service Composition with Replication 2

Population-Based Incremental Learning for Effective IoT Serv...

引用

2nd IEEE International Conference on Software Services Engineering (SSE)

作者： Sun, Fengyang Chen, Gang Ma, Hui Hartmann, Sven Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6012 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6012 New Zealand Tech Univ Clausthal Dept Informat D-38678 Clausthal Zellerfeld Germany

ISBN: (纸本)9798350368529;9798350368512

Internet of Things Service Composition (SCIoT) aims to find the best composite IoT service to fulfil users' requirements. Given the NP-hard complexity of SCIoT, Evolutionary Computation methods, especially estimation of distribution algorithms (EDAs), have received increasing attention to solve SCIoT problems. As one of the most popular EDA methods, Population-Based Incremental Learning (PBIL) has demonstrated its strong competency in optimising composed services in SCIoT. However, conventional PBIL does not explicitly utilize problem knowledge such as QoS and service replication, limiting its effectiveness for IoT service composition. In this paper, we propose a new PBIL based approach, named Population-Based Incremental Learning to Improve Service Composition (PBILISC), to solve the SCIoT problem. Different from traditional PBIL, PBILISC seamlessly integrates PBIL with QoS-aware local search (QLS) to effectively handle replicated services in the SCIoT problem. Specifically, PBILISC evolves a series of populations of solutions jointly through PBIL and QLS. PBIL leverages a probability distribution for population updates, while QLS focuses on improving the best evolved solution by searching promising neighboring solutions under the guidance of QoS. Experimental results show that PBILISC can outperform PBIL and several state-of-the-art methods on multiple benchmark SCIoT problems.

关键词： Service Composition Internet of Things Service Computing Combinatorial optimization estimation of distribution algorithm

来源：评论

学校读者我要写书评

暂无评论

Metaheuristics for Optimal Scheduling of Appliances in Energy Efficient Neighbourhoods 1

引用

20th EPIA Conference on Artificial Intelligence (EPIA)

作者： Alfageme, Amaia Esnaola-Gonzalez, Iker Javier Diez, Francisco Gilabert, Eduardo TEKNIKER Basque Res & Technol Alliance BRTA C Inaki Goenaga 5 Eibar 20600 Spain Univ Basque Country UPV EHU Fac Informat Paseo Manuel Lardizabal 1 Donostia San Sebastian 20018 Spain

ISBN: (数字)9783030862305

ISBN: (纸本)9783030862305;9783030862299

As a consequence of the continuous growth in the worldwide electricity consumption, supplying all customer electrical requests is becoming increasingly difficult for electricity companies. That is why, they encourage their clients to actively manage their own demand, providing several resources such us their Optimal Demand Profile (ODP). This profile provides to users a summary of the demand they should consume during the day. However, this profile needs to be translated into specific control actions first, such as the when each appliance should be used. In this article a comparison of the performance of two metaheuristic optimisation algorithms (Tabu Search and estimation of distribution algorithm (EDA)) and their variants for the calculation of optimal appliance scheduling is presented. Results show that Tabu Search algorithm can reach better feasible solutions at faster execution times than EDA does.

关键词： Appliance scheduling optimisation Optimal Demand Profile Tabu Search estimation of distribution algorithm

来源：评论

学校读者我要写书评

暂无评论

Evolving NK-complexity for Evolutionary Solvers 12

Evolving NK-complexity for Evolutionary Solvers

引用

14th International Conference on Genetic and Evolutionary Computation Conference (GECCO)

作者： Santana, Roberto Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU P Manuel Lardizabal 1 San Sebastian 20018 Spain

ISBN: (纸本)9781450311786

In this paper we empirically investigate the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms (EDAs). We evolve instances that maximize the EDA complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measure, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K.

关键词： estimation of distribution algorithm NK-model complexity analysis networks

来源：评论

学校读者我要写书评

暂无评论

Expanding variational autoencoders for learning and exploiting latent representations in search distributions 18

Expanding variational autoencoders for learning and exploiti...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Garciarena, Unai Santana, Roberto Mendiburu, Alexander Univ Basque Country Intelligent Syst Grp Donostia San Sebastian Spain

ISBN: (纸本)9781450356183

In the past, evolutionary algorithms (EAs) that use probabilistic modeling of the best solutions incorporated latent or hidden variables to the models as a more accurate way to represent the search distributions. Recently, a number of neural-network models that compute approximations of posterior (latent variable) distributions have been introduced. In this paper, we investigate the use of the variational autoencoder (VAE), a class of neural-network based generative model, for modeling and sampling search distributions as part of an estimation of distribution algorithm. We show that VAE can capture dependencies between decision variables and objectives. This feature is proven to improve the sampling capacity of model based EAs. Furthermore, we extend the original VAE model by adding a new, fitness-approximating network component. We show that it is possible to adapt the architecture of these models and we present evidence of how to extend VAEs to better fulfill the requirements of probabilistic modeling in EAs. While our results are not yet competitive with state of the art probabilistic-based optimizers, they represent a promising direction for the application of generative models within EDAs.

关键词： machine learning variational autoencoder estimation of distribution algorithm neural network generative modeling

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44页 << < 31 32 33 34 35 36 37 38 39 40 > >>

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：