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

3,080 篇 会议
141 册 图书
24 篇 期刊文献

馆藏范围

3,244 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,110 篇 工学
- 1,926 篇 计算机科学与技术...
- 1,072 篇 软件工程
- 412 篇 信息与通信工程
- 286 篇 电气工程
- 265 篇 生物工程
- 226 篇 控制科学与工程
- 169 篇 生物医学工程（可授...
- 81 篇 化学工程与技术
- 76 篇 机械工程
- 68 篇 光学工程
- 68 篇 仪器科学与技术
- 64 篇 网络空间安全
- 63 篇 电子科学与技术（可...
- 62 篇 安全科学与工程
- 57 篇 动力工程及工程热...
767 篇 理学
- 435 篇 数学
- 290 篇 生物学
- 149 篇 统计学（可授理学、...
- 137 篇 物理学
- 128 篇 系统科学
- 90 篇 化学
502 篇 管理学
- 304 篇 图书情报与档案管...
- 228 篇 管理科学与工程(可...
- 93 篇 工商管理
222 篇 医学
- 191 篇 临床医学
- 123 篇 基础医学(可授医学...
- 87 篇 药学(可授医学、理...
- 59 篇 公共卫生与预防医...
69 篇 法学
- 67 篇 社会学
52 篇 教育学
- 47 篇 教育学
35 篇 经济学
28 篇 农学
6 篇 艺术学
4 篇 军事学
1 篇 文学

主题

571 篇 data mining
546 篇 machine learning
241 篇 pattern recognit...
183 篇 deep learning
173 篇 feature extracti...
156 篇 artificial intel...
151 篇 support vector m...
114 篇 accuracy
113 篇 machine learning...
111 篇 training
93 篇 data models
82 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 centre of interd...
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 galgotias univer...
4 篇 zhejiang univers...
4 篇 maharishi school...
4 篇 department of cs...
4 篇 pune
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

3,100 篇 英文
135 篇 其他
32 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 3245 条记录，以下是431-440 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：

An Empirical Comparison of Support Vector machines Versus Nearest Neighbour Methods for machine learning Applications 3rd

An Empirical Comparison of Support Vector Machines Versus Ne...

引用

3rd international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Gamboni, Mori Garg, Abhijai Grishin, Oleg Oh, Seung Man Sowani, Francis Spalvieri-Kruse, Anthony Toussaint, Godfried T. Zhang, Lingliang New York Univ Abu Dhabi Fac Sci POB 129188 Abu Dhabi U Arab Emirates

ISBN: (纸本)9783319255309;9783319255293

Support vector machines (SVMs) are traditionally considered to be the best classifiers in terms of minimizing the empirical probability of misclassification, although they can be slow when the training datasets are large. Here SVMs are compared to the classic k-Nearest Neighbour (k-NN) decision rule using seven large real-world datasets obtained from the University of California at Irvine (UCI) machine learning Repository. To counterbalance the slowness of SVMs on large datasets, three simple and fast methods for reducing the size of the training data, and thus speeding up the SVMs are incorporated. One is blind random sampling. The other two are new linear-time methods for guided random sampling which we call Gaussian Condensing and Gaussian Smoothing. In spite of the speedups of SVMs obtained by incorporating Gaussian Smoothing and Condensing, the results obtained show that k-NN methods are superior to SVMs on most of the seven data sets used, and cast doubt on the general superiority of SVMs. Furthermore, random sampling works surprisingly well and is robust, suggesting that it is a worthwhile preprocessing step to either SVMs or k-NN.

关键词： machine learning data mining Support vector machines SMO Training data condensation k-Nearest neighbour methods Blind and guided random sampling Wilson editing Gaussian Condensing

来源：评论

学校读者我要写书评

暂无评论

Harmonic Representation for CNN-LSTM Automatic Chord recognition 3

Harmonic Representation for CNN-LSTM Automatic Chord Recogni...

引用

3rd international conference on Cybernetics and Intelligent System (ICORIS)

作者： Ito, Tsuyoshi Arai, Shuichi Tokyo City Univ Grad Sch Engn Setagaya Ku 1-28-1 Tamazutsumi Tokyo 1588557 Japan

ISBN: (纸本)9781665425803

Paper Since Chord progression is the element that determines the harmony of a piece of music, Automatic Chord recognition (ACR) from audio signals is a crucial task in the field of Music Information Retrieval(MIR). Recently, various models using deep learning have been proposed, but there are few studies on their input features. Notes parts of the chord are the fundamental note, and its overtone ringed simultaneously. In order to model these audio signals efficiently, feature transforms such as "Constat-Q-Transform(CQT)" is used. However, due to the super-position of fundamental notes and overtones of various instruments in polyphonic music, it is considered difficult to model chords even by deep learning. Therefore, we focused on the structure, including fundamental notes are on the logarithm and its overtones are on the linear. In this paper, we propose a feature representation that can represent overtone structure for each fundamental note. Based on these feature representations, data-driven approach to learn the chord by CNN-LSTM model. We evaluated performance using 383 songs with publicly available annotations, and achieved the same performance with approximately one-tenth of the number of parameters than the existing methods.

关键词： Deep learning Signal Processing pattern recognition Music Information Retrieval Automatic Chord recognition

来源：评论

学校读者我要写书评

暂无评论

Researches advanced in Generative Adversarial Networks 3

Researches advanced in Generative Adversarial Networks

引用

3rd international conference on machine learning, Big data and Business Intelligence (MLBDBI)

作者： Lei, Yilin Wang, Haozhen Xu, Zhiwen Cheltenham Ladies Coll Cheltenham Glos England Guangdong Expt Middle Sch Int Dept Guangzhou Peoples R China SCNU Int Dept Affiliated High Sch Chengdu Peoples R China

ISBN: (纸本)9781665417907

As a new unsupervised learning algorithm to model complex data in various applications, the Generative Adversarial Networks (GANs) have gained extensive recognition and become a research hot. Inspired by the two-person zero-sum game theory, the GANs methods have more powerful feature learning and feature expression ability than traditional machine learning algorithms, while still suffering from several challenges such as the non-convergence, vanishing, and exploding gradients, modal collapse, and instability problems at the model training stage. In this paper, we firstly introduce the theory and framework of GANs and analyze the causes for the above difficulties in detail. Then we survey a wide range of typical improved GANs models by summarizing their Advantages and limitations. Finally, we discuss the existing problems in the application and possible research areas in the future.

关键词： Deep learning Generative Adversarial Network Non-convergence Vanishing gradients

来源：评论

学校读者我要写书评

暂无评论

Application of Big data in the Research of Intelligent Information Management System 3

Application of Big Data in the Research of Intelligent Infor...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Sun, Qianqian Xue, Kunlun Huangdao District People's Hospital Shandong Qingdao266499 China Huangdao Branch Network Police Brigade Qingdao Municipal Public Security Bureau Shandong 266499 China

ISBN: (纸本)9798350354171

data analysis and mining play an important role in the research of intelligent information management system, but there is a problem of inaccurate information management. Traditional machine learning cannot solve the information management problem in the research of intelligent information management system, and the effect is not satisfactory. Therefore, this paper proposes the application of intelligent information management system based on big data, and the application of intelligent information management system research an analysis was carried out. Firstly, The key is your guiding theory, es, to reduce the interference factors in data analysis and mining. Then, the dataset theory is used to form a big data analysis and mining scheme, and the data analysis and mining results are comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, big data is better than traditional machine learning in terms of data analysis and mining accuracy, data analysis and mining influencing factor time. © 2024 IEEE.

关键词： data accuracy

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of machine learning Approaches on learning Management System data 3

A Comparative Study of Machine Learning Approaches on Learni...

引用

3rd international conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2019

作者： Oreski, Dijana Hajdin, Goran Faculty of Organization and Informatics University of Zagreb Pavlinska 2 Varaždin42000 Croatia

ISBN: (纸本)9781728135724

This paper addresses the analysis of machine learning (ML) effectiveness in learning analytics context. Four different machine learning approaches are evaluated. The results offer information about the usefulness of these approaches and help to decide which of the approaches is the most promising one in learning analytics application. Results substantiate that the neural networks ML model trained on our learning management system (LMS) data exhibits the best performance for predicting the students' academic performance. In our future research, predictive model results will be explained within a pedagogical context in order to be used as part of student support mechanism. © 2019 IEEE.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Prediction and assessment of student learning outcomes in calculus: A decision support of integrating data mining and Bayesian belief networks

Prediction and assessment of student learning outcomes in ca...

引用

2011 3rd international conference on Computer Research and Development, ICCrd 2011

作者： Liu, Kevin Fong-Rey Chen, Jia-Shen Department of Safety Health and Environmental Engineering Ming Chi University of Technology Taipei 24301 Taiwan

ISBN: (纸本)9781612848372

A decision support system based on data mining (DM) and Bayesian belief networks (BBN) is proposed to predict the student learning outcomes and takes the calculus course as an example to help students overcome their learning difficulties. Total of 427 freshmen in Ming Chi University of Technology (Taiwan) did questionnaires to assist this study. The methodologies involves four steps: fuzzy theory to identify the factors on learning outcomes;data mining to construct influence diagram;machine learning to establish the probability tables in BBN;and the model to predict the exam scores at the beginning of course and thereby to help students enhance their scores according to their weakness. © 2011 IEEE.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Relational Frequent patterns mining for Novelty Detection from data Streams

引用

6th international conference on machine learning and data mining in pattern recognition

作者： Ceci, Michelangelo Appice, Annalisa Loglisci, Corrado Caruso, Costantina Fumarola, Fabio Valente, Carmine Malerba, Donato Univ Bari Dipartimento Informat I-70126 Bari Italy

ISBN: (纸本)9783642030697

We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at consecutive time points. We extend previous solutions by considering the case of objects modeled by multiple database relations. Frequent relational patterns are efficiently extracted at each time point, and a time window is used to filter out novelty patterns. An application of the proposed algorithm to the problem of detecting anomalies in network traffic is described and quantitative and qualitative results obtained by analyzing real stream of data collected from the firewall logs are reported.

关键词： Filtration

来源：评论

学校读者我要写书评

暂无评论

Selection of Subsets of Ordered Features in machine learning

引用

6th international conference on machine learning and data mining in pattern recognition

作者： Seredin, O. Kopylov, A. Mottl, V. Tula State Univ Pr Lenina 92 Tula 300600 Russia Russian Acad Sci Ctr Comp Moscow 117969 Russia

ISBN: (纸本)9783642030697

The new approach of relevant feature selection in machine learning is proposed for the case of ordered features. Feature selection and regularization of decision rule are combined in a single procedure. The selection of features is realized by introducing weight coefficients, characterizing degree of relevance of respective feature. A priori information about feature ordering is taken into account in the form of quadratic penalty or in the form of absolute Value penalty on the difference of weight coefficients of neighboring features. Study of a penalty function in the form of absolute value shows computational complexity Of Such formulation. The effective method of solution is proposed. The brief survey of authors early papers. the mathematical frameworks, and experimental results are provided.

关键词： machine learning feature selection ordered features regularization of training support vector machines parametric dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Annotating Documents using Active learning Methods for a Maintenance Analysis Application 20

Annotating Documents using Active Learning Methods for a Mai...

引用

3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Pope, James Terwilliger, Mark Connell, J. A. (Jim) Talley, Gabriel Blozik, Nicholas Taylor, David Univ North Alabama Florence AL 35632 USA Univ Montevallo Montevallo AL USA Univ Memphis Memphis TN 38152 USA

ISBN: (纸本)9781450375511

The aircraft cargo industry still maintains vast amounts of the maintenance history of aircraft components in electronic (i.e. scanned) but unsearchable images. For a given supplier, there can be hundreds of thousands of image documents only some of which contain useful information. Using supervised machine learning techniques has been shown to be effective in recognising these documents for further information extraction. A well known deficiency of supervised learning approaches is that annotating sufficient documents to create an effective model requires valuable human effort. This paper first shows how to obtain a representative sample from a supplier's corpus. Given this sample of unlabelled documents an active learning approach is used to select which documents to annotate first using a normalised certainty measure derived from a soft classifier's prediction distribution. Finally the accuracy of various selection approaches using this certainty measure are compared along each iteration of the active learning cycle. The experiments show that a greedy selection method using the uncertainty measure can significantly reduce the number of annotations required for a certain accuracy. The results provide valuable information for users and more generally illustrate an effective deployment of a machine learning application.

关键词： Active learning Document Classification

来源：评论

学校读者我要写书评

暂无评论

Real Time Facial Emotion recognition Methods using Different machine learning Techniques 3

Real Time Facial Emotion Recognition Methods using Different...

引用

3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Prabha, K.R. Nataraj, B. Ajaydevan, R. Kabilan, S. Muthuselvam, V. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Artificial intelligence has faced a rapid growth and it has made a huge change in this world. In real time the traditional algorithm has go phut to reach the human demands. Due to this great success has been gained by machine and deep learning in many different implementations such as parameter classification system, output recommendation system, different pattern recognition, etc. The thoughts, behavior and feeling of a human can be determined by his/her emotions. By utilizing the characteristics of deep learning/machine learning and different methods such as input analysis, face unlocking, etc. A facial emotions recognition system can be built and can be implemented with good accuracy. The project mainly focuses on comparison of different available deep and machine learning algorithm and find different metrices like matrix, accuracy, estimated time, validation time, etc. © 2022 IEEE.

关键词： Emotion recognition

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325页 << < 40 41 42 43 44 45 46 47 48 49 > >>

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：