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

78 篇 会议
10 篇 期刊文献

馆藏范围

88 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

58 篇 工学
- 50 篇 计算机科学与技术...
- 48 篇 软件工程
- 30 篇 控制科学与工程
- 9 篇 信息与通信工程
- 3 篇 机械工程
- 2 篇 化学工程与技术
- 1 篇 动力工程及工程热...
- 1 篇 建筑学
- 1 篇 土木工程
- 1 篇 农业工程
- 1 篇 环境科学与工程（可...
37 篇 管理学
- 29 篇 管理科学与工程(可...
- 13 篇 图书情报与档案管...
- 1 篇 工商管理
35 篇 理学
- 30 篇 数学
- 9 篇 系统科学
- 6 篇 统计学（可授理学、...
- 2 篇 化学
- 1 篇 物理学
1 篇 文学
- 1 篇 新闻传播学
1 篇 农学
- 1 篇 作物学

主题

10 篇 support vector m...
4 篇 rough set
4 篇 fuzzy measure
4 篇 decision trees
3 篇 support vector m...
3 篇 random variables
3 篇 dominance relati...
3 篇 fuzzy integral
3 篇 classification a...
3 篇 classification (...
3 篇 training
2 篇 information syst...
2 篇 core vector mach...
2 篇 rough sets
2 篇 approximation al...
2 篇 core set
2 篇 noise measuremen...
2 篇 decision tree
2 篇 variable precisi...
2 篇 extended dominan...

机构

23 篇 key lab. of mach...
9 篇 key lab.of machi...
6 篇 college of mathe...
6 篇 key lab.of machi...
5 篇 key lab.in machi...
4 篇 key lab of machi...
4 篇 key lab. of mach...
4 篇 key lab. of mach...
3 篇 key lab. in mach...
3 篇 college of scien...
3 篇 center for machi...
3 篇 department of in...
3 篇 key lab. in mach...
2 篇 beijing key lab ...
2 篇 key laboratory o...
2 篇 college of mathe...
2 篇 key lab. in mach...
2 篇 key lab of high ...
2 篇 beijing academy ...
2 篇 graduate univers...

作者

9 篇 yan li
9 篇 li yan
8 篇 xi-zhao wang
5 篇 yuan fang
4 篇 yan zhan
4 篇 jun-hai zhai
4 篇 su-fang zhang
4 篇 jun-fen chen
3 篇 li zhao
3 篇 zhan yan
3 篇 guo-fang zhang
3 篇 wang xizhao
3 篇 zhai jun-hai
3 篇 lu shuxia
3 篇 qun-feng zhang
3 篇 chen hao
3 篇 shuxia lu
3 篇 ai-xia chen
3 篇 zhai junhai
3 篇 ning li

语言

87 篇 英文
1 篇 其他

检索条件"机构=Key Lab.for Machine Learning and Computational Intelligence"

共 88 条记录，以下是51-60 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：

Local, Mid-Level and Convolutional Features Fusion Using Multiple Kernel learning for Image Classification

Local, Mid-Level and Convolutional Features Fusion Using Mul...

引用

IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Yao Lu Hui Zhang Bojun Xie Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China

Feature representation and feature fusion are important factors in image classification problem. In this paper, the local features, mid-level features and convolutional features are combined using the multiple kernel learning method. Experimental results show that the local features, mid-level features and convolutional features can be fused effectively to improve the classification performance about 4%-6% on several popular benchmarks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Unsupervised feature selection based on feature relevance

Unsupervised feature selection based on feature relevance

引用

International Conference on machine learning and Cybernetics (ICMLC)

作者： Feng Zhang Ya-Jun Zhao Jun-Fen Key Lab. of Machine Learning and computational Intelligence Hebei University Baoding China College of Physics Science and Technology Hebei University Baoding China Key Lab. of Machine Learning and computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

Feature selection is an essential technique used in data mining and machine learning. Many feature selection methods have been studied for supervised problems. However feature selection for unsupervised learning is rarely studied. In this paper, we proposed an approach to select features for unsupervised problems. Firstly, the original features are clustered according to their relevance degree defined by mutual information. And then the most informative feature is selected from each cluster based on the contribution-information of each feature. The experimental results show that the proposed method can match some popular supervised feature selection methods. And the features selected by our method do include most of the information hidden in the overall original features.

关键词： machine learning Mutual information Random variables Entropy Unsupervised learning Information theory Cybernetics Educational institutions Data mining computational intelligence

来源：评论

学校读者我要写书评

暂无评论

An improved differential evolution and its application to determining feature weights in similarity based clustering

An improved differential evolution and its application to de...

引用

International Conference on machine learning and Cybernetics (ICMLC)

作者： Chun-Ru Dong Daniel S. Yeung Xi-Zhao Wang Key Lab. of Machine Learning and Computational Intelligence Hebei University Baoding China Machine Learning and Cybernetics Research Center South China University of Technology Guangzhou China

ISBN: (纸本)9781479902590

Feature weighting, which is considered as an extension of feature selection techniques, has been successfully applied to improve the performance of clustering. Focusing on the clustering based on a similarity matrix, we design an optimization model to minimize the fuzziness of similarity matrix by learning feature weights. The objective of this model is to get a more reasonable result of clustering through minimizing the uncertainty (fuzziness and non-specificity) of similarity matrix. To solving this optimization model effectively, we propose a new searching approach which integrates together multiple evolution strategies of both differential evolution and dynamic differential evolution. The experimental results on several benchmark datasets show that the performance of the proposed method is significantly improved compared to that of gradient-descent-based approach in terms of five selected clustering evaluation indices, i.e., fuzziness of similarity matrix, intra-class similarity, inter-class similarity, ratio of intra-class similarity to inter-class similarity.

关键词： Abstracts Noise measurement

来源：评论

学校读者我要写书评

暂无评论

SOME PROPERTIES OF OPERATIONS ON TYPE-2 FUZZY SETS

SOME PROPERTIES OF OPERATIONS ON TYPE-2 FUZZY SETS

引用

2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： SHUO WANG SHU-MING WANG YING LIU Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

In this study, we study set operations on type-2 fuzzy sets. We first discuss join and meet operations of membership grades of type-2 fuzzy sets under left continuous t-norms and derive distributive law of type-2 fuzzy sets. Then, some properties on compositions of fuzzy relations is discussed. We derived that the distributive laws under union and composition of type-2 fuzzy relations is valid. An example shows the failure of distributive laws under intersection and composition.

关键词： Composition Fuzzy relation Operation Type-2 fuzzy set Triangular norm

来源：评论

学校读者我要写书评

暂无评论

APPLICATION OF INTELLIGENT DECISION SUPPORT TECHNOLOGY IN POWER SYSTEM

APPLICATION OF INTELLIGENT DECISION SUPPORT TECHNOLOGY IN PO...

引用

2009 International Conference on machine learning and Cybernetics(2009机器学习与控制论国际会议)

作者： HAO CHEN YAN ZHAN HAI-YAN LIU Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

Distribution network cabling planning is a very complex project This paper proposes the application of intelligent decision support technology in Power System. By adding a module library and the concept of model management systems, Intelligent Power Service System realizes intelligence decision support in the distribution network power cabling planning by using dynamic programming, spatial data mining and decision tree techniques, and has a certain amount of self-learning ability.

关键词： Intelligent Decision Support System Decision tree Power cabling scheme GIS

来源：评论

学校读者我要写书评

暂无评论

AN INEXACT REASONING ALGORITHM BASED ON INTERACTION WITH FUZZY RULE MATRIX TRANSFORMATION

AN INEXACT REASONING ALGORITHM BASED ON INTERACTION WITH FUZ...

引用

2009 International Conference on machine learning and Cybernetics(2009机器学习与控制论国际会议)

作者： AI-XIA CHEN NING LI LI ZHAO HONG-TAO ZHU GUO-FANG ZHANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 China

This paper presents a reasoning algorithm based on interaction with fuzzy rule matrix transformation, and applies it to completing the patterns. Then the new full patterns will be used in training and synthetic judgment The investigation shows that the method is effective and may be widely used in Reasoning with Incomplete Knowledge.

关键词： Fuzzy measure Interaction Fuzzy integral Fuzzy rule matriz transformation Inezact reasoning algorithm

来源：评论

学校读者我要写书评

暂无评论

A NEW HEURISTIC OF THE DECISION TREE INDUCTION

A NEW HEURISTIC OF THE DECISION TREE INDUCTION

引用

2009 International Conference on machine learning and Cybernetics(2009机器学习与控制论国际会议)

作者： NING LI LI ZHAO AI-XIA CHEN QING-WU MENG GUO-FANG ZHANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

Decision tree induction is one of the useful approaches for extracting classification knowledge from a set of feature-based instances. The most popular heuristic information used in the decision tree generation is the minimum entropy. This heuristic information has a serious disadvantage-the poor generalization capability [3]. Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. It has good generalization. Considering the relationship between the classification margin of support vector machine(SVM) and the generalization capability, the large margin of SVM can be used as the heuristic information of decision tree, in order to improve its generalization *** paper proposes a decision tree induction algorithm based on large margin heuristic. Comparing with the binary decision tree using the minimum entropy as the heuristic information, the experiments show that the generalization capability has been improved by using the new heuristic.

关键词： Decision tree Support vector machines SMO Clustering large margin Generalization

来源：评论

学校读者我要写书评

暂无评论

A NEW ALGORITHM FOR SOLVING CONVEX HULL PROBLEM AND ITS APPLICATION TO FEATURE SELECTION

A NEW ALGORITHM FOR SOLVING CONVEX HULL PROBLEM AND ITS APPL...

引用

2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： FENG GUO XI-ZHAO WANG YAN LI Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

A new method to solve the convex hull problem in n-dimensional spaces is proposed in this paper. At each step, a new point is added into the convex hull if the point is judged to be out of the current convex hull by a linear programming model. For the linear separable classification problem, if an instance is regarded as a point of the instances space, the overlap does not still occur between the convex hulls of different classes after a feature is deleted, then we can delete that feature. Repeat this process, an algorithm for feature selection is given. Experimental results show the effectiveness of the algorithm.

关键词： Convez Hull Linear Programming Problem Feature Selection

来源：评论

学校读者我要写书评

暂无评论

NRMCS: NOISE REMOVING BASED ON THE MCS

NRMCS: NOISE REMOVING BASED ON THE MCS

引用

2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： XI-ZHAO WANG BO WU YU-LIN HE XIANG-HAO PEI Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

MCS (Minimal Consistent Set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect the size of minimal consistent set. Therefore, removing noise is an important issue before sample selection. In this paper, an improvement approach based on MCS to select the representative samples is proposed. Compared with other algorithms which remove the noise by Wilson Editing in advance for the representative samples selection, this algorithm performs the processes of noise removing and samples selection simultaneously. According to this method, most noise can be deleted and the most representative samples can be identified and retained. The experiments show that the proposed method can greatly remove the redundant samples and noise as well as increase the accuracy of solutions when it is used for classification tasks.

关键词： MCS Sample Selection ICF Representative Subset Noise Wilson Editing

来源：评论

学校读者我要写书评

暂无评论

Parallel implementation of apriori algorithm based on MapReduce

Parallel implementation of apriori algorithm based on MapRed...

引用

13th ACIS International Conference on Software Engineering, Artificial intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012

作者： Li, Ning Zeng, Li He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100139 China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9780769547619

Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorithms that can handle large volumes of data becomes a challenging task due to the large databases. In this paper, we implement a parallel Apriori algorithm based on MapReduce, which is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes). The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. © 2012 IEEE.

关键词： MapReduce

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9页 << < 1 2 3 4 5 6 7 8 9 > >>

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：