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

3,117 篇 会议
141 册 图书
24 篇 期刊文献

馆藏范围

3,281 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,141 篇 工学
- 1,954 篇 计算机科学与技术...
- 1,095 篇 软件工程
- 405 篇 信息与通信工程
- 287 篇 电气工程
- 267 篇 生物工程
- 217 篇 控制科学与工程
- 172 篇 生物医学工程（可授...
- 79 篇 化学工程与技术
- 73 篇 光学工程
- 72 篇 机械工程
- 68 篇 仪器科学与技术
- 65 篇 电子科学与技术（可...
- 62 篇 安全科学与工程
- 55 篇 动力工程及工程热...
- 54 篇 网络空间安全
777 篇 理学
- 430 篇 数学
- 296 篇 生物学
- 151 篇 统计学（可授理学、...
- 147 篇 物理学
- 127 篇 系统科学
- 90 篇 化学
505 篇 管理学
- 306 篇 图书情报与档案管...
- 231 篇 管理科学与工程(可...
- 95 篇 工商管理
218 篇 医学
- 193 篇 临床医学
- 125 篇 基础医学(可授医学...
- 87 篇 药学(可授医学、理...
- 55 篇 公共卫生与预防医...
72 篇 法学
- 70 篇 社会学
52 篇 教育学
- 47 篇 教育学
35 篇 经济学
31 篇 农学
6 篇 艺术学
4 篇 军事学
1 篇 文学

主题

568 篇 data mining
542 篇 machine learning
241 篇 pattern recognit...
195 篇 deep learning
174 篇 feature extracti...
154 篇 artificial intel...
153 篇 support vector m...
113 篇 accuracy
113 篇 machine learning...
111 篇 training
93 篇 data models
80 篇 neural networks
77 篇 data mining and ...
65 篇 classification
62 篇 sentiment analys...
61 篇 classification a...
59 篇 big data
57 篇 predictive model...
50 篇 face recognition
48 篇 prediction algor...

机构

7 篇 maharishi school...
6 篇 guangdong univer...
5 篇 galgotias univer...
5 篇 centre of interd...
5 篇 pune
5 篇 chitkara univers...
5 篇 uttaranchal univ...
5 篇 uttaranchal inst...
5 篇 institute of aut...
4 篇 shanghai jiao to...
4 篇 chitkara univers...
4 篇 school of inform...
4 篇 indian statistic...
4 篇 south china univ...
4 篇 zhejiang univers...
4 篇 maharishi school...
4 篇 department of cs...
4 篇 university of ca...
4 篇 school of comput...
4 篇 department of co...

作者

9 篇 de-shuang huang
7 篇 coenen frans
7 篇 venkatesh svetha
7 篇 petra perner
7 篇 tianrui li
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 jacek m. zurada
6 篇 krasotkina olga
6 篇 lotfi a. zadeh
6 篇 mottl vadim
6 篇 leszek rutkowski
5 篇 vitoantonio bevi...
5 篇 sankar k. pal
4 篇 waiyamai kitsana
4 篇 sozo inoue
4 篇 sergei o. kuznet...
4 篇 duoqian miao
4 篇 shengli xie
4 篇 hepu deng

语言

3,063 篇 英文
209 篇 其他
38 篇 中文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3282 条记录，以下是2821-2830 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：

Observability of information in databases - New spins in data warehousing for credit risk management

Observability of information in databases - New spins in dat...

引用

3rd international conference on Software and data Technologies

作者： Pavlek, Stjepan Kalpic, Damir Univ Zagreb Fac Comp & Elect Engn Zagreb 41000 Croatia

ISBN: (纸本)9789898111531

In this paper the observability of information in modem databases is investigated. Observable information is the one that is explicitly stored in a database. Unobservable information is information hidden in various coding schemes, transaction streams or free-text-description fields. Nowadays credit risk management tends to employ cutting edge technologies and approaches from the fields of statistics and machine learning for achieving their goals. Often it is forgotten that machine learning schemes can only use completely observable information. The issue is eventually addressed - but instead when using the data it should be addressed during the data warehouse design phase.

关键词： observability data warehousing data mining free text fields coding schemes chart of accounts

来源：评论

学校读者我要写书评

暂无评论

learning Web Page Block Functions using Roles of Images

Learning Web Page Block Functions using Roles of Images

引用

3rd international conference on Pervasive Computing and Applications

作者： Yang, Xin Shi, Yuanchun Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9781424420209

Making use of block information in Web IR and data mining tasks calls for a good understanding of the function of each block. Existing works on classifying block functions and judging block importance have not made full use of the image factor, and only simple image features were considered. We regard image as a strong indicator of Web page blocks with various functions and propose to learn block functions using roles of images as part of block features. Blocks are generated from Web page segmentation and roles of images are automatically decided by image classification. We experiment on 140 Web pages and demonstrate that utilizing roles of images can significantly improve the classification quality of learning Web page block functions. We also measure the usefulness of different roles of images and evaluate the effect of two page segmentation methods.

关键词： Web Page Block Block Function Role of Image machine learning

来源：评论

学校读者我要写书评

暂无评论

Sentiment Classification for Chinese Reviews Using machine learning Methods Based on String Kernel

Sentiment Classification for Chinese Reviews Using Machine L...

引用

3rd international conference on Convergence and Hybrid Information Technology (ICCIT 2008)

作者： Zhang, Changli Zuo, Wanli Peng, Tao He, Fengling Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9780769534077

Sentiment classification aims at mining reviews of people for a certain event's topic or product by automatic classifying the reviews into positive or negative opinions. With the fast developing of World Wide Web applications, sentiment classification would have huge opportunity to help people automatic analysis of customers' opinions from the web information. Automatic opinion mining will benefit to both decision maker and ordinary people. Up to now, it is still a complicated task with great challenge. There are mainly two types of approaches for sentiment classification, machine learning methods and semantic orientation methods. Though some pioneer researches explored the approaches for English reviews classification, few jobs have been done on sentiment classification for Chinese reviews. The machine learning approach Based on string kernel for sentiment classification on reviews written in Chinese was proposed in this paper. data experiment shows the capability of this approach.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Distributed Clustering for data Sources with Diverse Schema

Distributed Clustering for Data Sources with Diverse Schema

引用

3rd international conference on Convergence and Hybrid Information Technology (ICCIT 2008)

作者： Visalakshi, N. Karthikeyani Thangavel, K. Alagambigai, P. Vellalar Coll Women Dept Comp Sci Erode 9 Tamil Nadu India Periyar Univ Dept Comp Sci Salem Tamil Nadu India

ISBN: (纸本)9780769534077

Many enterprises incorporate information gathered from a variety of data sources into an integrated input for some learning task. For example, aiming towards the design of an automated diagnostic tool for some diseases, one may wish to integrate data gathered from many different hospitals. Analyzing and mining these distributed heterogeneous data sources require distributed machine learning and data mining technique In this paper, a Modified Distributed Combining Algorithm is proposed to cluster disparate data sources having diverse, possibly overlapping set of features and also need not share objects. First, all objects located at local sites are grouped using K-Means/Fuzzy C-Means clustering algorithm and resulting centroid is considered as local models. Then, the set of centroids are transformed into unified structure and optimum values are assigned to missing attributes. Finally, global cluster centroid is computed to identify global cluster model based on cluster ensemble and centroid mapping. The experiments are carried out for various datasets of UCI machine learning data repository in order to achieve the efficiency of the proposed algorithm.

关键词： Distributed Clustering Global Centroid Diverse Schema K-Means Local Centroid

来源：评论

学校读者我要写书评

暂无评论

Grip-pattern recognition in smart gun based on likelihood-ratio classifier and support vector machine

引用

3rd international conference on Image and Signal Processing

作者： Shang, Xiaoxin Veldhuis, Raymond N. J. Univ Twente Signals & Syst Grp NL-7500 AE Enschede Netherlands

ISBN: (纸本)9783540699040

In the biometric verification system of a smart gun, the rightful user of a gun is recognized based on grip-pattern recognition. It was found that the verification performance of this system degrades strongly when the data for training and testing have been recorded in different sessions with a time lapse. This is due to the variations between the probe image and the gallery image of a subject. In this work the grip-pattern verification has been implemented based on both classifiers of the likelihood-ratio classifier and the support vector machine. It has been shown that the support vector machine gives much better results than the likelihood-ratio classifier if there are considerable variations between data for training and testing. However, once the variations are reduced by certain techniques and thus the data are better modelled during the training process, the support vector machine tends to lose its superiority.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Incorporating a new relational feature in Arabic online handwritten character recognition

Incorporating a new relational feature in Arabic online hand...

引用

3rd international conference on Computer Vision Theory and Applications

作者： Izadi, Sara Suen, Ching Y. Ctr Pattern Recognit & Machine Intelligence Montreal PQ Canada

ISBN: (纸本)9789898111210

Artificial neural networks have shown good performance in classification tasks. However, models used for learning in pattern classification are challenged when the differences between the patterns of the training set are small. Therefore, the choice of effective features is mandatory for obtaining good performance. Statistical and geometrical features alone are not suitable for recognition of hand printed characters due to variations in writing styles that may result in deformations of character shapes. We address this problem by using a relational context feature combined with a local descriptor for training a neural network-based recognition system in a user-independent online character recognition application. Our feature extraction approach provides a rich representation of the global shape characteristics, in a considerably compact form. This new relational feature provides a higher distinctiveness and increases robustness with respect to character deformations. While enhancing the recognition accuracy, the feature extraction is computationally simple. We show that the ability to discriminate in Arabic handwriting characters is increased by adopting this mechanism in feed forward neural network architecture. Our experiments on Arabic character recognition show comparable results with the state-of-the-art methods for online recognition of these characters.

关键词： feature extraction online handwriting neural network recognition

来源：评论

学校读者我要写书评

暂无评论

Extracting Chinese Multi-word Terms from Small Corpus

Extracting Chinese Multi-word Terms from Small Corpus

引用

3rd international conference on Intelligent System and Knowledge Engineering

作者： Lang, Zhou Liang, Zhang Chong, Feng Heyan, Huang Nanjing Univ Sci Technol Coll Comp Sci & Technol Nanjing 210094 Peoples R China Nanjing Univ Dept Comp Sci & Technol Nanjing 210093 Peoples R China Res Ctr Comp & Language Informat Engn CAS Beijing 100089 Peoples R China

ISBN: (纸本)9781424421961

In this paper, we present an automatic terminology extraction approach for Chinese multi-word terms. In this term extraction system, besides five linguistic rules acquired from an available term list by some machine learning methods, two statistical strategies are involved: a termhood measure based on the term distribution variation, and a unithood measure adopting the left and right entropy method to estimate the collocation variation degree. The candidates are ranked according to the values of the former. The latter is used to filter the preposition phrases and some verb-object phrases that rarely appear as terms. By validating on a small scale corpus in the computer domain, the precision reaches 91.5% of the top 2000 outputs.

关键词： data mining Terminology Knowledge engineering Filters Intelligent systems Computer science Measurement units Statistical analysis Testing Natural languages

来源：评论

学校读者我要写书评

暂无评论

Protein Expression Molecular pattern Discovery by Nonnegative Principal Component Analysis

引用

3rd IAPR international conference on pattern recognition in Bioinformatics

作者： Han, Xiaoxu Scazzero, Joseph Eastern Michigan Univ Dept Math & Bioinformat Program Ypsilanti MI 48197 USA Eastern Michigan Univ Dept Accout & Finance Ypsilanti MI USA

ISBN: (纸本)9783540884347

Identifying cancer molecular patterns robustly from large dimensional protein expression data not only has significant impacts on clinical ontology, but also presents a challenge for statistical learning. Principal component analysis (PCA) is a widely used feature selection algorithm and generally integrated with classic classification algorithms to conduct cancer molecular pattern discovery. However, its holistic mechanism prevents local data characteristics capture in feature selection. This may lead to the increase of misclassification rates and affect robustness of cancer molecular diagnostics. In this study, we develop a nonnegative principal component analysis (NPCA) algorithm and propose a NPCA-based SVM algorithm with sparse coding in the cancer molecular pattern analysis of proteomics data. We report leading classification results from this novel algorithm in predicting cancer molecular patterns of three benchmark proteomics datasets, under 100 trials of 50% hold-out and leave one out cross validations, by directly comparing its performances with those of the PCA-SVM, NMF-SVM, SVM, k-NN and PCA-LDA classification algorithms with respect to classification rates, sensitivities and specificities. Our algorithm also overcomes the overfitting problem in the SVM and PCA-SVM classifications and provides exceptional sensitivities and specificities.

关键词： Nonnegative principle component analysis sparse coding support vector machine (SVM)

来源：评论

学校读者我要写书评

暂无评论

Feature Selection and Classification for Small Gene Sets

引用

3rd IAPR international conference on pattern recognition in Bioinformatics

作者： Stiglic, Gregor Rodriguez, Juan J. Kokol, Peter Univ Maribor Fac Hlth Sci Zitna Ulica 15 SLO-2000 Maribor Slovenia Univ Maribor Fac Elect Engn & Comp Sci Zitna Ulica 15 SLO-2000 Maribor Slovenia Univ Burgos Burgos 09006 Spain

ISBN: (纸本)9783540884347

Random Forests, Support Vector machines and k-Nearest Neighbors are successful and proven classification techniques that are widely used for different kinds of classification problems. One of them is classification of genomic and proteomic data that is known as a problem with extremely high dimensionality and therefore demands suited classification techniques. In this domain they are usually combined with gene selection techniques to provide optimal classification accuracy rates. Another reason for reducing the dimensionality of such datasets is their interpretability. It is much easier to interpret a small set of ranked genes than 20 or 30 thousands of unordered genes. In this paper we present a classification ensemble of decision trees called Rotation Forest and evaluate its classification performance on small subsets of ranked genes for 14 genomic and proteomic classification problems. An important feature of Rotation Forest is demonstrated - i.e. robustness and high classification accuracy using small sets of genes.

关键词： Gene expression analysis machine learning feature selection ensemble of classifiers

来源：评论

学校读者我要写书评

暂无评论

Sequence Based Prediction of Protein Mutant Stability and Discrimination of Thermophilic Proteins

引用

3rd IAPR international conference on pattern recognition in Bioinformatics

作者： Gromiha, M. Michael Huang, Liang-Tsung Lai, Lien-Fu Natl Inst Adv Ind Sci & Technol Computat Biol Res Ctr Koto Ku AIST Tokyo Waterfront Bio IT Res Bldg2-42 Aomi Tokyo 1350064 Japan MingDao Univ Dept Comp Sci & Informat Engn Changhua 523 Taiwan Natl Changhua Univ Educ Dept Comp Sci & Informat Engn Changhua 523 Taiwan

ISBN: (纸本)9783540884347

Prediction of protein stability upon amino acid substitution and discrimination of thermophilic proteins from mesophilic ones are important problems in designing stable proteins. We have developed a classification rule generator using the information about wild-type, mutant, three neighboring residues and experimentally observed stability data. Utilizing the rules, we have developed a method based on decision tree for discriminating the stabilizing and destabilizing mutants and predicting protein stability changes upon single point mutations, which showed an accuracy of 82% and a correlation of 0.70, respectively. In addition, we have systematically analyzed the characteristic features of amino acid residues in 3075 mesophilic and 1609 thermophilic proteins belonging to 9 and 15 families, respectively, and developed methods for discriminating them. The method based on neural network could discriminate them at the 5-fold cross-validation accuracy of 89% in a dataset of 4684 proteins and 91% in a test set of 707 proteins.

关键词： Protein stability rule generator discrimination prediction thermophilic proteins neural network machine learning techniques

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329页 << < 279 280 281 282 283 284 285 286 287 288 > >>

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：