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

91 篇 会议
24 篇 期刊文献

馆藏范围

115 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

70 篇 工学
- 60 篇 计算机科学与技术...
- 54 篇 软件工程
- 33 篇 控制科学与工程
- 14 篇 信息与通信工程
- 4 篇 机械工程
- 4 篇 化学工程与技术
- 2 篇 动力工程及工程热...
- 2 篇 生物工程
- 1 篇 光学工程
- 1 篇 冶金工程
- 1 篇 电气工程
- 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...
- 1 篇 安全科学与工程
48 篇 理学
- 39 篇 数学
- 11 篇 系统科学
- 11 篇 统计学（可授理学、...
- 4 篇 化学
- 3 篇 物理学
- 2 篇 生物学
- 1 篇 大气科学
43 篇 管理学
- 31 篇 管理科学与工程(可...
- 17 篇 图书情报与档案管...
- 2 篇 工商管理
1 篇 文学
- 1 篇 新闻传播学
1 篇 农学
- 1 篇 作物学

主题

14 篇 support vector m...
6 篇 training
4 篇 rough set
4 篇 fuzzy measure
4 篇 decision trees
4 篇 classification a...
3 篇 support vector m...
3 篇 educational inst...
3 篇 optimization
3 篇 random variables
3 篇 dominance relati...
3 篇 data mining
3 篇 fuzzy integral
3 篇 feature extracti...
3 篇 extreme learning...
3 篇 classification (...
3 篇 accuracy
2 篇 information syst...
2 篇 learning systems
2 篇 core vector mach...

机构

23 篇 key lab. of mach...
8 篇 college of mathe...
8 篇 key lab.of machi...
7 篇 college of mathe...
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...
4 篇 key laboratory o...
3 篇 key laboratory o...
3 篇 key lab. in mach...
3 篇 college of scien...
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...

作者

12 篇 li yan
10 篇 yan li
8 篇 xi-zhao wang
7 篇 bai ruipu
7 篇 xing hong-jie
5 篇 yuan fang
5 篇 zhai junhai
4 篇 yan zhan
4 篇 jun-hai zhai
4 篇 lu shuxia
4 篇 su-fang zhang
4 篇 wang xi-zhao
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 qun-feng zhang
3 篇 chen hao

语言

114 篇 英文
1 篇 其他

检索条件"机构=Key Lab. in Machine Learning and Computational Intelligence of Hebei Province"

共 115 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：

Robust smooth one-class support vector machine

Robust smooth one-class support vector machine

引用

International Conference on Information Technology and Electronic Commerce

作者： Jin-Kou Hu Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Hebei Province China

ISBN: (纸本)9781479953004

In this paper, a novel one-class classification approach, namely, robust smooth one-class support vector machine (RSOCSVM) is proposed. The proposed method can efficiently enhance the anti-noise ability of the traditional one-class support vector machine (OCSVM). Utilizing the smooth technique, RSOCSVM reformulates the quadratic programming problem of OCSVM as an unstrained optimization format. Moreover, half-quadratic minimization is used to solve the obtained unstrained optimization problem. Experimental results on two synthetic data sets and nine benchmark data sets demonstrate that the proposed method is superior to the traditional OCSVM.

关键词： Optimization Support vector machines Robustness Accuracy Minimization Training Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

A HSC-based sample selection method for Support Vector machine

A HSC-based sample selection method for Support Vector Machi...

引用

International Conference on machine learning and Cybernetics

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

ISBN: (纸本)9781424465262

Support Vector machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Correntropy based self-organizing map

Correntropy based self-organizing map

引用

International Conference on machine learning and Cybernetics (ICMLC)

作者： Qing-Zhen Shang Hong-Jie Xing Key Laboratory of Machine Learning and Computational Intelligence Hebei University Baoding Province China

ISBN: (纸本)9781509003914

Self-organizing map (SOM) is regarded as a type of feedfoward neural network. It has been successfully used for unsupervised learning. However, the objective function of the traditional SOM relies on the mean squared error (MSE) criterion, which makes the performance of SOM become poor in the presence of noise. In the paper, correntropy based measure is proposed to substitute MSE to enhance the anti-noise ability of SOM. Moreover, the half-quadratic optimization technique is utilized to deal with the corresponding optimization problem. Experimental results demonstrate that the proposed method achieves better anti-noise performance in comparison with the traditional SOM.

关键词： Neurons Distributed databases Optimization Topology Kernel Cybernetics Self-organizing feature maps

来源：评论

学校读者我要写书评

暂无评论

A Total Error Rate Multi-class Classification

A Total Error Rate Multi-class Classification

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Xizhao Wang Meng Zhang Shuxia Lu Xu Zhou Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University

ISBN: (纸本)9781467317139

The total error rate (TER) has been presented as a minimum classification error model for the single-layer feedforward network (SLFN) learning. The TEE, which uses one-against-all (OAA) for multi-class classification, may cause unbalanced data set especially for large number of training data in multi-class classification and then often has a bad influence on the accuracy. This paper proposes a new method, called multi-class total error rate (MTER) to deal with this problem. The MTER, which uses a unified learning mode of regression and multi-class classification and minimizes the error rate for each class, can approximate any target functions. It implies that a balanced data set can be obtained and the training process can be simplified. Experiments show that MTER has a higher accuracy and lower computational complexity in comparison with some learning algorithms such as ELM and TER. The experiments also show that the MTER has a similar performance with LIBSVM.

关键词： Extreme learning machine One-against-all Tatal error rate Multi-class classification Support vector machine Phyla multi-class Global Error Dataset Error analysis learning algorithms Ecosystem models Elm TECR gene

来源：评论

学校读者我要写书评

暂无评论

Second-Order Convolutional Neural Network Based on Cholesky Compression Strategy 21st

Second-Order Convolutional Neural Network Based on Cholesky ...

引用

21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Li, Yan Zhang, Jing Hua, Qiang Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province College of Mathematics and Information Science Hebei University Baoding071002 China Research Center for Applied Mathematics and Interdisciplinary Sciences Beijing Normal University at Zhuhai Beijing519087 China

ISBN: (纸本)9783030692438

In the past few years, Convolution Neural Network (CNN) has been successfully applied to many computer vision tasks. Most of these networks can only extract first-order information from input images. The second-order statistical information refers to the second-order correlation obtained by calculating the covariance matrix, the fisher information matrix, or the vector outer product operation on the local feature group according to the channels. It has been shown that using second-order information on facial expression datasets can better capture the distortion of facial area features, while at the same time generate more parameters which may cause much more computational cost. In this article we propose a new CNN structure including layers which can (i) incorporate first-order information into the covariance matrix;(ii) use eigenvalue vectors to measure the importance of feature channels;(iii) reduce the bilinear dimensionality of the parameter matrix;and (iv) perform Cholesky decomposition on the positive definite matrix to complete the compression of the second-order information matrix. Due to the incorporation of both first-order and second-order information and the Cholesky compression strategy, our proposed method reduces the number of parameters by half of the SPDNet model, and simultaneously achieves better results in facial expression classification tasks than the corresponding first-order model and the reference second-order model. © 2021, Springer Nature Switzerland AG.

关键词： Covariance matrix

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

Support vector machine for suppressing error method

Support vector machine for suppressing error method

引用

1st International Conference on Information Science and Engineering, ICISE2009

作者： Lu, Shuxia Shi, Pu Chen, Ming Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China Baoding Pascali Cable TV Integrater Information Network Co. Ltd. Baoding China College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9780769538877

Support Vector machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of [0, 1]. The separation hypersurface is simplified and the margin of hypersurface is widened. Experimental results show that our proposed method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

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

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

PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

引用

7th IFIP International Conference on Intelligent Information Processing, IIP 2012

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

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalab.lity of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information Processing.

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12页 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：