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

1,730 篇 会议
45 册 图书
11 篇 期刊文献

馆藏范围

1,785 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

1,223 篇 工学
- 1,093 篇 计算机科学与技术...
- 628 篇 软件工程
- 254 篇 信息与通信工程
- 199 篇 电气工程
- 177 篇 生物工程
- 99 篇 控制科学与工程
- 98 篇 生物医学工程（可授...
- 60 篇 光学工程
- 46 篇 化学工程与技术
- 45 篇 电子科学与技术（可...
- 41 篇 机械工程
- 40 篇 仪器科学与技术
- 40 篇 安全科学与工程
- 29 篇 交通运输工程
- 28 篇 动力工程及工程热...
- 27 篇 材料科学与工程（可...
455 篇 理学
- 199 篇 数学
- 196 篇 生物学
- 144 篇 物理学
- 83 篇 统计学（可授理学、...
- 51 篇 系统科学
- 45 篇 化学
233 篇 管理学
- 167 篇 图书情报与档案管...
- 86 篇 管理科学与工程(可...
- 43 篇 工商管理
182 篇 医学
- 160 篇 临床医学
- 72 篇 基础医学(可授医学...
- 39 篇 药学(可授医学、理...
- 32 篇 公共卫生与预防医...
34 篇 法学
- 30 篇 社会学
17 篇 经济学
14 篇 教育学
11 篇 农学
2 篇 艺术学
1 篇 军事学

主题

239 篇 machine learning
184 篇 deep learning
140 篇 pattern recognit...
110 篇 feature extracti...
91 篇 support vector m...
88 篇 data mining
79 篇 accuracy
75 篇 training
59 篇 convolutional ne...
51 篇 machine learning...
48 篇 data models
46 篇 artificial intel...
41 篇 computational mo...
40 篇 neural networks
39 篇 face recognition
36 篇 learning systems
36 篇 adversarial mach...
33 篇 predictive model...
32 篇 classification
32 篇 image recognitio...

机构

6 篇 college of compu...
5 篇 xi'an university...
4 篇 badji mokhtar un...
4 篇 shanghai jiao to...
4 篇 institute of big...
4 篇 chitkara centre ...
4 篇 chitkara univers...
4 篇 library shanghai...
3 篇 school of comput...
3 篇 univ chittagong ...
3 篇 chongqing univer...
3 篇 centre of resear...
3 篇 school of comput...
3 篇 school of comput...
3 篇 school of design...
3 篇 korea univ dept ...
3 篇 school of automa...
3 篇 university of mi...
3 篇 shenyang jianzhu...
3 篇 xi'an university...

作者

5 篇 venkatesh svetha
4 篇 ashwini kodipall...
4 篇 zhang yan
4 篇 srinivasa gowri
4 篇 kamel mohamed s.
4 篇 lall brejesh
4 篇 trupthi rao
3 篇 andersson karl
3 篇 peng yang
3 篇 malerba donato
3 篇 karl andersson
3 篇 chaudhury santan...
3 篇 de-shuang huang
3 篇 sekhar c. chandr...
3 篇 wang shuo
3 篇 kamata sei-ichir...
3 篇 mohammad shahada...
3 篇 azizi nabiha
3 篇 phung dinh
3 篇 krasotkina olga

语言

1,587 篇 英文
190 篇 其他
26 篇 中文

检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1786 条记录，以下是1351-1360 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：

Optimizing spatial filters for the extraction of envelope-coupled neural oscillations

Optimizing spatial filters for the extraction of envelope-co...

引用

4th international Workshop on pattern recognition in Neuroimaging (PRNI)

作者： Daehne, Sven Nikulin, Vadim Ramirez, David Schreier, Peter J. Mueller, Klaus-Robert Haufe, Stefan Berlin Inst Technol Dept Machine Learning Berlin Germany Charite Dept Neurol Berlin Germany Univ Paderborn Dept Elect Engn & Informat Technol Paderborn Germany CUNY City Coll Dept Biomed Engn New York NY USA Korea Univ Dept Brain & Cognit Engn Seoul South Korea

ISBN: (纸本)9781479941490

Amplitude-to-amplitude interactions between neural oscillations are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro/magnetencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low.

关键词： Brain modeling Correlation Couplings data models Electroencephalography Oscillators Signal to noise ratio signal to noise ratios Brain modeling data models Couplings Oscillators Spatial filters Electroencephalography Oscillations coupling

来源：评论

学校读者我要写书评

暂无评论

An assessment method of the data similarity considering the shape differences of objects

An assessment method of the data similarity considering the ...

引用

4th international conference on Frontiers of Manufacturing Science and Measuring Technology, ICFMM 2014

作者： Li, Zhong Li, Guo Ping Liu, Yang Yang School of Electrical and Electronic Engineering North China Electric Power University Baoding 071003 China

ISBN: (纸本)9783038351924

the measure of similarity is an essential basic work in the field of pattern recognition, machine learning and data mining, etc. the shape similarity distance (SSD) which considering vector difference factors in the calculation of similarity, is an amendment of the classical distance. Based on the classical Euclidean distance, Manhattan distance and the SSD, this paper completed the clustering on the multiple datasets that contained shape features data of objects. the experimental results indicated that compared with the classical distances, the SSD can consider the characteristics of shape of theses objects to estimate their similarity. © (2014) Trans Tech Publications, Switzerland.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Mining strong valid association rule from frequent pattern and infrequent pattern based on min-max sinc constraints

Mining strong valid association rule from frequent pattern a...

引用

2014 4th international conference on Communication Systems and Network Technologies, CSNT 2014

作者： Poundekar, Mukesh Manekar, Amitkumar S. Baghel, Mukesh Gupta, Hitesh Department of Computer Science and Engineering PCST Bhopal India

ISBN: (纸本)9781479930708

Rule mining is very efficient technique for find relation of correlated data. the correlation of data gives meaning full extraction process. For the mining of rule mining a variety of algorithm are used such as Apriori algorithm and tree based algorithm. Some algorithm is wonder performance but generate negative association rule and also suffered from multi-scan problem. In this paper we proposed IMLMS-PANR-GA association rule mining based on min-max algorithm and MLMS formula. In this method we used a multi-level multiple support of data table as 0 and 1. the divided process reduces the scanning time of database. the proposed algorithm is a combination of MLMS and min-max algorithm. Support length key is a vector value given by the transaction data set. the process of rule optimization we used min-max algorithm and for evaluate algorithm conducted the real world dataset such as heart disease data and some standard data used from UCI machine learning repository. © 2014 IEEE.

关键词： Association rules

来源：评论

学校读者我要写书评

暂无评论

A Robust One-Class Classification Model with Interval-Valued data Based on Belief Functions and Minimax Strategy

A Robust One-Class Classification Model with Interval-Valued...

引用

10th international conference on machine learning and data Mining (MLDM)

作者： Utkin, Lev V. Zhuk, Yulia A. Chekh, Anatoly I. St Petersburg State Forest Tech Univ Dept Control Automat & Syst Anal St Petersburg Russia Saint Petersburg State Elect Univ Dept Comp Sci St Petersburg Russia

ISBN: (纸本)9783319089799;9783319089782

A robust model for solving the one-class classification problem by interval-valued training data is proposed in the paper. It is based on using Dempster-Shafer theory for getting the lower and upper risk measures. the minimax or pessimistic strategy is applied to state an optimization problem in the framework of the modified support vector machine (SVM). the algorithm for computing optimal parameters of the classification function stems from extreme points of the convex polytope produced by the interval-valued elements of a training set. It is shown that the hard non-linear optimization problem is reduced to a finite number of standard SVMs.

关键词： novelty detection support vector machine kernel interval-valued data minimax strategy quadratic programming Dempster-Shafer theory

来源：评论

学校读者我要写书评

暂无评论

An Evaluation of data Mining Classification Models for Network Intrusion Detection

An Evaluation of Data Mining Classification Models for Netwo...

引用

4th international conference on Digital Information and Communication Technology and it's Applications (DICTAP)

作者： So-In, Chakchai Mongkonchai, Nutakarn Aimtongkham, Phet Wijitsopon, Kasidit Rujirakul, Kanokmon Khon Kaen Univ Fac Sci Dept Comp Sci Appl Network Technol ANT Lab Khon Kaen 40002 Thailand

ISBN: (纸本)9781479937240

Due to a rapid growth of Internet, the number of network attacks has risen leading to the essentials of network intrusion detection systems (IDS) to secure the network. With heterogeneous accesses and huge traffic volumes, several pattern identification techniques have been brought into the research community. data Mining is one of the analyses which many IDSs have adopted as an attack recognition scheme. thus, in this paper, the classification methodology including attribute and data selections was drawn based on the well-known classification schemes, i.e., Decision Tree, Ripper Rule, Neural Networks, Naive Bayes, k-Nearest-Neighbour, and Support Vector machine, for intrusion detection analysis using both KDD CUP dataset and recent HTTP BOTNET attacks. Performance of the evaluation was measured using recent Weka tools with a standard cross-validation and confusion matrix.

关键词： BOTNET Classification data Mining Intrusion Detection KDD CUP dataset Network Security

来源：评论

学校读者我要写书评

暂无评论

A Pragmatic Approach to Predict Hardware Failures in Storage Systems using MPP database and Big data Technologies

A Pragmatic Approach to Predict Hardware Failures in Storage...

引用

4th IEEE international Advance Computing conference (IACC)

作者： Kumar, Rohit Vijayakumar, Senthilkumar Ahamed, Syed Azar TATA Consultancy Serv Ltd Bangalore Karnataka India

ISBN: (纸本)9781479925711

A storage system in a data center consists of various components such as Disk Array Enclosure (DAE), disks, processors, servers, hosts running different applications, and so on. Hard disk and server failures are not frequent but are often very costly. Such failures can have a very adverse effect on the business of an organization. the ability to accurately predict an impending disk or server failure can add an essential functionality for designing a reliable, fault tolerant and continuously available storage system. this paper explains a novel approach to predict hardware failures using spectrum-kernel Parallel Support Vector machine (Parallel SVM) method by analyzing the system events logged in the system log files. these log files not only records the events processed by the system but it also holds the messages as the system state changes. A single message in the system log file is insufficient for any prediction and such prediction is bound to be less accurate. the approach introduced in the paper uses a sequence or pattern of messages from the system log file using a Sliding Window of messages with window size of 5 message sequence to predict the likelihood of a failure. these Sliding Windows of message sequences acts as inputs to the Parallel SVM. the Parallel SVM further tags the messages to a failure or non-failure system. data Mining techniques are used in extracting useful information from the raw dataset. A solutioning model is developed using the structured dataset and machine learning algorithms. this environment when implemented using actual system logs from Linux-based storage system have shown to predict a hardware failure with accuracy of 90-92 percent.

关键词： Parallel ATM Classification Hard disk & Server failure prediction Cloud Computing Big data Analytics machine learning MPP database

来源：评论

学校读者我要写书评

暂无评论

ACCD: Associative Classification over Concept-Drifting data Streams

ACCD: Associative Classification over Concept-Drifting Data ...

引用

10th international conference on machine learning and data Mining (MLDM)

作者： Waiyamai, Kitsana Kangkachit, thanapat Saengthongloun, Bordin Rakthanmanon, thanawin Kasetsart Univ Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9783319089799;9783319089782

Associative classification has shown good result over many classification techniques on static datasets. However, little work has been done on associative classification over data streams. Different from data in traditional static databases, data streams typically arrive continuously and unboundedly with occasionally changing data distribution known as concept drift. In this paper, we propose a new Associative Classification over Concept-Drifting data streams, ACCD. ACCD is able to accurately detect concept drift in data streams and reduce its effect by using an ensemble of classifiers. A mechanism for statistical accuracy bounds estimation is used for supporting concept-drift detection. With this mechanism, accuracy recovering time is decreased and a situation where ensemble of classifiers drops accuracy is avoided. Compared to AC-DS (first technique on associative classification algorithm over data streams), AUEH (Accuracy updated ensemble with Hoeffding tree) and VFDT(Very Fast Decision Trees) on 4 real-world data stream datasets, ACCD exhibits the best performance in terms of accuracy.

关键词： concept drift changing environments data streams classification associative classification multiple class-sssociation rules

来源：评论

学校读者我要写书评

暂无评论

Research on database and Information Technology in the Decision Tree of Communication Construction Scheme 4

Research on Database and Information Technology in the Decis...

引用

4th international conference on Automation, Communication, Architectonics and Materials (ACAM)

作者： Peng, Yongjun Peng, Yang Liu, Cifang Acad Natl Def Informat Wuhan 430010 Peoples R China Hubei Transportat Res Inst Wuhan 430030 Peoples R China

ISBN: (纸本)9783038352891

With the development of database technology as well as the widespread application of database management system, our capabilities of both generating and collecting data have been increasing rapidly. In addition, popular use of the World Wide Web as a global information system has flooded us with a tremendous amount of data and information. this explosive growth in stored or transient data has generated an urgent need for new techniques and automated tools that can intelligently assist us in transforming the vast amounts of data into useful information and knowledge. the data Mining technology brought forward. data mining, also popularly referred to knowledge discovery from data (KDD), is the automated or convenient extraction of patterns representing knowledge implicitly stored or captured in large databases, data warehouses, the Web, other massive information repositories, or data streams. data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization. database and Information Technology in the Decision Tree of is very important for the military. the data Mining had been applied and studied in these years, and it has been applied in many domains successfully, such as business, finance and medical treatment. However, little is applied in communication construction scheme.

关键词： Decision tree data mining KDD

来源：评论

学校读者我要写书评

暂无评论

Application and research of data mining technology in communication network environment

Application and research of data mining technology in commun...

引用

4th international conference on Materials Science and Information Technology, MSIT 2014

作者： Zhi, Zhang Jiangxi Science and Technology Normal University Technique Center of Modern Education Jiangx i Nanchang 330013 China

ISBN: (纸本)9783038351733

the abnormal data of communication networks are complex and diverse, it is difficult to recognize and mine the abnormal data accurately and effectively with traditional methods. In order to improve the recognition accuracy of communication network, a data mining algorithms based on the method of communication network abnormal data recognition is presented. Firstly, the communication network data samples are analyzed for fuzzy c-means clustering in order to obtain the degree of membership matrix. Secondly, the training samples of communication network abnormal data mining are selected according to the membership. At last, the training samples are put into the least square support vector machine learning, which establish the model of abnormal data identification in communication network. the performance of the algorithm was tested by simulation tests, and the results show that, the abnormal data recognition efficiency and accuracy in this paper was improved much more than the traditional identification methods. © (2014) Trans Tech Publications, Switzerland.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Gas recognition Under Sensor Drift by Using Deep learning 1

引用

8th international conference on Intelligent Systems and Knowledge Engineering (ISKE)

作者： Hu, Xiaonan Liu, Qihe Cai, Hongbin Li, Fan Univ Elect Sci & Technol China Sch Comp Sci & Engn Digital Media Technol Key Lab Sichuan Prov Chengdu 611731 Peoples R China

ISBN: (数字)9783642549274

ISBN: (纸本)9783642549274;9783642549267

machine olfaction is an intelligent system that combines cross-sensitivity chemical sensor array and an effective pattern recognition algorithm for the detection, identification, or quantification of various odors. data collected by the sensor array are multivariate time-series signals with complex structure, and these signals become more difficult to analyze due to sensor drift. In this work, we focus on improving the classification performance under sensor drift by using the deep learning method, which is popular among these years. Compared with other methods, our method can effectively tackle sensor drift by automatically extracting features, thus not only removing the complexity of designing the hand-made features, but also making it pervasive for a variety of application in machine olfaction. Our experimental results show that the deep learning method can learn the features that are more robust to drift than the original input and achieves high classification accuracy.

关键词： Sensor drift Deep learning machine olfaction

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179页 << < 132 133 134 135 136 137 138 139 140 141 > >>

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：