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

23,359 篇 会议
2,934 篇 期刊文献
16 篇 学位论文

馆藏范围

26,309 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

5,648 篇 工学
- 3,392 篇 计算机科学与技术...
- 3,142 篇 电气工程
- 1,808 篇 信息与通信工程
- 640 篇 控制科学与工程
- 538 篇 软件工程
- 481 篇 仪器科学与技术
- 300 篇 机械工程
- 300 篇 测绘科学与技术
- 284 篇 生物医学工程（可授...
- 148 篇 电子科学与技术（可...
- 114 篇 生物工程
- 108 篇 交通运输工程
- 107 篇 环境科学与工程（可...
- 75 篇 网络空间安全
- 56 篇 航空宇航科学与技...
- 43 篇 材料科学与工程（可...
841 篇 理学
- 194 篇 物理学
- 158 篇 生物学
- 137 篇 地球物理学
- 124 篇 数学
- 122 篇 系统科学
- 96 篇 地理学
- 50 篇 化学
- 42 篇 统计学（可授理学、...
526 篇 管理学
- 481 篇 管理科学与工程(可...
493 篇 医学
- 369 篇 临床医学
- 100 篇 基础医学(可授医学...
195 篇 农学
- 99 篇 作物学
- 80 篇 农业资源与环境
75 篇 文学
- 70 篇 新闻传播学
23 篇 教育学
21 篇 经济学
19 篇 艺术学
13 篇 法学
7 篇 军事学
2 篇 历史学

主题

26,309 篇 classification a...
6,463 篇 feature extracti...
5,177 篇 training
4,677 篇 accuracy
4,372 篇 support vector m...
3,194 篇 machine learning...
2,671 篇 prediction algor...
2,638 篇 clustering algor...
2,490 篇 algorithm design...
2,350 篇 data mining
2,253 篇 machine learning
1,593 篇 data models
1,402 篇 deep learning
1,358 篇 optimization
1,338 篇 decision trees
1,172 篇 predictive model...
1,164 篇 testing
1,138 篇 computational mo...
1,094 篇 neural networks
1,020 篇 databases

机构

63 篇 chitkara univers...
23 篇 centre of resear...
23 篇 college of infor...
19 篇 school of comput...
18 篇 tsinghua univers...
18 篇 university of ch...
17 篇 chitkara centre ...
17 篇 school of inform...
17 篇 department of co...
16 篇 school of comput...
16 篇 school of comput...
16 篇 school of electr...
14 篇 computer science...
14 篇 univ chinese aca...
14 篇 microsoft resear...
14 篇 school of comput...
13 篇 school of electr...
13 篇 school of electr...
13 篇 school of comput...
12 篇 department of in...

作者

29 篇 wei li
22 篇 hui wang
22 篇 jun wang
21 篇 lei wang
17 篇 xia sun
17 篇 yang yang
16 篇 jing zhang
15 篇 jun zhang
15 篇 liu yang
14 篇 xi chen
14 篇 li zhang
14 篇 jie li
13 篇 jian wang
13 篇 rakesh kumar
13 篇 jie yang
13 篇 jun shi
13 篇 chen chen
12 篇 bing xue
12 篇 ahmad jalal
12 篇 xuegang hu

语言

26,172 篇 英文
91 篇 其他
33 篇 中文
4 篇 土耳其文
2 篇 法文
1 篇 西班牙文
1 篇 俄文

检索条件"主题词=CLASSIFICATION algorithms"

共 26309 条记录，以下是4991-5000 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：

classification Method of Indoor Environmental Data based on Internet of Things

Classification Method of Indoor Environmental Data based on ...

引用

Data Driven Control and Learning Systems (DDCLS)

作者： JiaWei Fan Haijiang Zhu Xuejing Wang College of Information & Technology Beijing University of Chemical Technology Beijing China Center for Information Technology Beijing University of Chemical Technology Beijing CO China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Aiming at the multi-source heterogeneous characteristics of data collected by Internet of Things wireless sensors, we investigate an IoT data classification method based on Improved Multi-Kernel Learning Support Vector Machine (IMKL-SVM). Kernel function types and parameters are selected mainly according to rule of thumb in traditional multi-kernel learning method. Our improved method has two steps in determining types and parameters of the kernel function. First, the primarily types and parameters of the kernel function is determined by a cross-validation method. And then the types and parameters of the multi-kernel function is optimized by SVM. The experiment designed two sets of data including four sensor data: temperature, humidity, light and atmospheric pressure. One group data was labeled as four types: morning, mid-afternoon, evening and night. And another group data was labeled as three types: day, evening and night. The classification accuracy are presented using the improved method, single-kernel SVM and traditional MKL-SVM for the two group data. Experiment results indicate that the IMKL-SVM method achieves higher classification accuracy for IoT data with multi-source heterogeneous characteristics.

关键词： Kernel Support vector machines Internet of Things Sensors Learning systems classification algorithms Data mining

来源：评论

学校读者我要写书评

暂无评论

Parametric Analysis on Crop/Weed classification via Optimized Convolutional Neural Network

Parametric Analysis on Crop/Weed Classification via Optimize...

引用

Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Bikramaditya Panda Manoj Kumar Mishra Bhabani Shankar Prasad Mishra Abhinandan Kumar Tiwari School of CE KIIT University Bhubaneswar Odisha India

ISBN: (数字)9781665419604

ISBN: (纸本)9781665429986

A new crop/weed classification model is established in this work that includes three main phases like (1) Preprocessing (2) Feature extraction (3) and classification Initially, the input image is pre-processed via contrast enhancement process. Subsequently, feature extraction is performed, where “Gray Level Co-occurrence Matrix (GLCM) as well as Gray-Level Run-Length Matrix (GRLM)” based features are extracted. These extracted features together with RGB image (totally 5 channels) are classified via “optimized Convolutional Neural Network (CNN) For enhancing the classification, the weight and activation function of CNN are chosen optimally via Hybridized Whale and Sea Lion Algorithm (HW-SLA) model. Eventually, algorithmic analysis is carried out on proposed HW-SLA algorithm by varying its parameters.

关键词： Training Support vector machines Whales Feature extraction classification algorithms Convolutional neural networks Tuning

来源：评论

学校读者我要写书评

暂无评论

Mining classification rules via an apriori approach

Mining classification rules via an apriori approach

引用

International Conference on Computer and Information Technology (ICCIT)

作者： S M Monzurur Rahman Mohammed Rokibul Alam Kotwal Xinghuo Yu Department of CSE United International University Dhaka Bangladesh School of Electrical and Computer Engineering RMIT University Melbourne Australia

classification rules are the interest of most data miners to summarize the discrimination ability of classes present in data. A classification rule is an assertion, which discriminates the concepts of one class from other classes. The most classification rules mining algorithm aims to providing a single solution where multiple solutions exist. Moreover, it does not guarantee the optimal solution and user has not any control over the classification error rate. In this paper, we addressed these problems inherent in mostly used classification algorithms. A solution has been proposed to solve these problems and it has been tested with experimental data.

关键词： classification algorithms Birds Data mining classification tree analysis Artificial neural networks

来源：评论

学校读者我要写书评

暂无评论

From Data to Diagnosis: Employing Machine Learning with LightGBM classification to Evaluate Autism Probability

From Data to Diagnosis: Employing Machine Learning with Ligh...

引用

Innovations and Challenges in Emerging Technologies (ICICET), International Conference on

作者： Khushi Mittal Kanwarpartap Singh Gill Deepak Upadhyay Sarishma Dangi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350319019

ISBN: (纸本)9798350319026

This study delves into the use of machine learning, namely the LightGBM classification algorithm, to evaluate the prevalence of autism. Timely and accurate diagnosis is crucial for the optimal management of Autism Spectrum Disorder (ASD), a complicated neurodevelopmental illness that can manifest in a wide range of severity. Conventional approaches to diagnosis depend on time-consuming and subjective clinical assessments. In order to train and improve a LightGBM classification model, the current work used a comprehensive dataset that included a wide range of demographic and clinical variables. The algorithm’s exceptional accuracy in predicting, flexibility in dealing with non-linear patterns, and capacity to manage complicated data linkages were the deciding factors in its selection. Results in terms of sensitivity, specificity, and overall performance metrics show that the model is successful in accurately categorising persons with and without autism, according to the research. The results show that machine learning, namely the approach, can improve the accuracy and reliability of autism diagnosis. Better outcomes for autistic people may emerge from the creation of early intervention techniques. To improve the model’s adaptability to different settings and facilitate its incorporation into healthcare, more study and verification are necessary. The suggested LightGBM Classifier has a 93% classification accuracy rate.

关键词： Autism Technological innovation Accuracy Sensitivity Sociology Machine learning classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Data and Image classification of Haematococcus pluvialis Based on SVM Algorithm

Data and Image Classification of Haematococcus pluvialis Bas...

引用

Chinese Automation Congress (CAC)

作者： Mengyu Yang Shigang Cui Yongli Zhang Jingyu Zhang Xinqi Li Tianjin Key Laboratory of Information Sensing &Intelligent Control Tianjin University of Technology and Education Tianjin China

ISBN: (纸本)9781665426480

The growth process of Haematococcus pluvialis includes cell proliferation stage and astaxanthin accumulation stage. There are differences in cell radius, cell color and cell number between the two stages. In this paper, cell images and cell data of two growth stages were collected, and SVM algorithm was used to establish two classification models of data and image of Haematococcus pluvialis cell. 30% of the data in the samples were selected as the test set. In cell data binary classification problem, SVM classification algorithm was used, and the final classification result was 0.855; In the problem of image binary classification, the accuracy of the model is tested by the method of ten-fold cross validation, and the ROC curve and AUC area are used to evaluate the model. Finally, the classification result of the model for Haematococcus pluvialis cell image is 0.90, and the performance is good.

关键词： Support vector machines Automation Image color analysis Data models classification algorithms Partitioning algorithms Image classification

来源：评论

学校读者我要写书评

暂无评论

Power System Events classification Technology Based on Deep-Learning

Power System Events Classification Technology Based on Deep-...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Xin Lei Hongwei Ma Bin Liu Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing China College of Information Science and Engineering China University of Petroleum (Beijing) Beijing China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

As the complexity of the power system continues to increase, the frequency of the power system anomalies is on the rise. These anomalies have significant and widespread impacts on the stability of the power grid. Therefore, the rapid and accurate classification of these anomalies is crucial in preventing their further propagation and mitigating potential economic losses. This study presents an algorithm based on Phasor Measurement Unit (PMU) data for monitoring the state of power systems and identifying the types of anomalies. First, a dataset for anomaly event classification is created based on PMU data, which is used to train and validate the anomaly event classification model. Subsequently, a robust anomaly event classification model is constructed, consisting of a residual module with one-dimensional Convolutional Neural Networks (CNN) and a cascaded fully connected neural network classifier. This algorithm has undergone rigorous testing in the IEEE New England 39 bus test system, demonstrating exceptional event recognition accuracy.

关键词： Accuracy Power system stability Propagation losses Phasor measurement units Data models Stability analysis classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Rationalization of Hematological Analysis of Diseases under Machine Learning Based classification Modeling Studies

Rationalization of Hematological Analysis of Diseases under ...

引用

Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Jinsheng Li College of Physics Zhengzhou University Zhengzhou China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

classification models are an important part of machine learning and are widely used in fields such as text analysis and medical diagnosis. This paper reviews the current popular classification algorithms, including BP neural network, support vector machine, random forest, particle swarm optimization BP neural network and extreme learning machine. By analyzing the characteristics, advantages and disadvantages, and applicable scenarios of each type of algorithms are in hematological analysis of diseases[1]. We discuss their performance and future development prospects in the field of hematological analysis. Machine learning models can be used as an auxiliary tool to help clinicians make more accurate diagnosis of hematological conditions and improve diagnostic efficiency, which is expected to be widely used in medical experiments in the future.

关键词： Support vector machines Analytical models Machine learning algorithms Text analysis Computational modeling Neural networks Vectors classification algorithms Medical diagnostic imaging Diseases

来源：评论

学校读者我要写书评

暂无评论

Cost-Sensitive Online classification with Adaptive Regularization and Its Applications

Cost-Sensitive Online Classification with Adaptive Regulariz...

引用

IEEE International Conference on Data Mining (ICDM)

作者： Peilin Zhao Furen Zhuang Min Wu Xiao-Li Li Steven C. H. Hoi Data Analytics Department Institute for Infocomm Research Singapore School of Information Systems Singapore Management University Singapore Singapore Management University Singapore Singapore SG

ISBN: (纸本)9781467395052

Cost-Sensitive Online classification is recently proposed to directly online optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. However, the previous existing learning algorithms only utilized the first order information of the data stream. This is insufficient, as recent studies have proved that incorporating second order information could yield significant improvements on the prediction model. Hence, we propose a novel cost-sensitive online classification algorithm with adaptive regularization. We theoretically analyzed the proposed algorithm and empirically validated its effectiveness with extensive experiments. We also demonstrate the application of the proposed technique for solving several online anomaly detection tasks, showing that the proposed technique could be an effective tool to tackle cost-sensitive online classification tasks in various application domains.

关键词： Prediction algorithms Data mining classification algorithms Machine learning algorithms Sensitivity Adaptation models Training

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Multi-Pipeline Architecture for Terabit Packet classification

Energy-Efficient Multi-Pipeline Architecture for Terabit Pac...

引用

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

作者： Weirong Jiang Viktor K. Prasanna Ming Hsieh Department of Electrical Engineering University of Southern California Los Angeles CA USA

Energy efficiency has become a critical concern in designing high speed packet classification engines for next generation routers. Although TCAM-based solutions can provide high throughput, they are not scalable with respect to power consumption. On the other hand, mapping decision-tree-based packet classification algorithms onto SRAM-based pipeline architectures becomes a promising alternative to TCAMs. However, existing SRAM-based algorithmic solutions need a variable number of accesses to large memories to classify a packet, and thus suffer from high energy dissipation in the worst case. This paper proposes a partitioning-based multi-pipeline architecture for energy-efficient packet classification. We optimize the HyperCuts algorithm, which is considered among the most scalable packet classification algorithms, and build a decision tree with a bounded height. Then we study two different schemes to partition the decision tree into several disjoint subtrees and map them onto multiple SRAM-based pipelines. Only one pipeline is active for classifying each packet, which takes a bounded number of accesses to small memories. Thus the energy dissipation is reduced. Simulation experiments using both real-life and synthetic traces show that the proposed architecture with 8 pipelines can store up to 10 K unique rules in 0.336 MB SRAM, sustains 1 Tbps throughput, and achieves 2.25-fold reduction in energy dissipation over the baseline pipeline architecture that is not partitioned.

关键词： Energy efficiency Pipelines Partitioning algorithms Energy dissipation Throughput classification algorithms Decision trees Engines Energy consumption classification tree analysis

来源：评论

学校读者我要写书评

暂无评论

Tooth brushing Pattern classification using Three-Axis Accelerometer and Magnetic Sensor for Smart Toothbrush

Tooth brushing Pattern Classification using Three-Axis Accel...

引用

29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), vol.10

作者： Kang-Hwi Lee Jeong-Whan Lee Kyeong-Seop Kim Dong-Jun Kim Kyungho Kim Heui-Kyung Yang Keesam Jeong Byungchae Lee Biomedical Engineering Department Konkuk University Chungli Taiwan School of Electronics and Information Engineering Cheongju University Cheonghu South Korea Electronic Engineering Department Dankook University Cheonan South Korea Information & Telecommunication Engineering Department Cheongju University Cheonghu South Korea Medical Informatics Department Yongin-Songdam College Yongin si South Korea Medical Informatics Depart Yongin-Songdam College Yongin si South Korea

The concept of intelligent toothbrush, capable of monitoring brushing motion, orientation through the grip axis, during tooth brushing was suggested in our previous study. In this study, we describe a tooth brushing pattern classification algorithm using three-axis accelerometer and three-axis magnetic sensor. We have found that inappropriate tooth brushing pattern showed specific moving patterns. In order to trace the position and orientation of toothbrush in a mouth, we need to know absolute coordinate information of toothbrush. By applying tilt-compensated azimuth (heading) calculation algorithm, which is generally used in small telematics devices, we could find the inclination and orientation information of toothbrush. To assess the feasibility of the proposed algorithm, 8 brushing patterns were preformed by 6 individual healthy subjects. The proposed algorithm showed the detection ratio of 98%. This study showed that the proposed monitoring system was conceived to aid dental care personnel in patient education and instruction in oral hygiene regarding brushing style.

关键词： Teeth Pattern classification Accelerometers Magnetic sensors Intelligent sensors Monitoring classification algorithms Mouth Azimuth Telematics

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500页 << < 491 492 493 494 495 496 497 498 499 500 > >>

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：