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

550 篇 会议
28 篇 期刊文献
7 册 图书

馆藏范围

585 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

470 篇 工学
- 425 篇 计算机科学与技术...
- 199 篇 软件工程
- 110 篇 电气工程
- 67 篇 信息与通信工程
- 45 篇 生物工程
- 42 篇 生物医学工程（可授...
- 26 篇 控制科学与工程
- 20 篇 光学工程
- 13 篇 仪器科学与技术
- 11 篇 机械工程
- 10 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 电子科学与技术（可...
- 7 篇 土木工程
140 篇 理学
- 69 篇 数学
- 56 篇 生物学
- 33 篇 物理学
- 24 篇 统计学（可授理学、...
- 19 篇 系统科学
- 8 篇 化学
90 篇 管理学
- 63 篇 图书情报与档案管...
- 35 篇 管理科学与工程(可...
- 19 篇 工商管理
46 篇 医学
- 36 篇 临床医学
- 22 篇 基础医学(可授医学...
- 13 篇 药学(可授医学、理...
- 7 篇 特种医学
- 6 篇 公共卫生与预防医...
11 篇 法学
- 9 篇 社会学
11 篇 教育学
- 10 篇 教育学
8 篇 经济学
6 篇 农学

主题

74 篇 machine learning
55 篇 data mining
36 篇 pattern recognit...
33 篇 deep learning
20 篇 feature extracti...
18 篇 artificial intel...
16 篇 support vector m...
15 篇 learning systems
11 篇 image classifica...
9 篇 training
8 篇 neural networks
8 篇 face recognition
8 篇 manifold learnin...
8 篇 supervised learn...
7 篇 pattern classifi...
7 篇 big data
7 篇 clustering algor...
7 篇 classification (...
7 篇 machine learning...
7 篇 data models

机构

4 篇 ukrainian cathol...
3 篇 harbin inst tech...
3 篇 guilin univ elec...
3 篇 fujian univ tech...
3 篇 college of compu...
3 篇 kunming univ col...
3 篇 nanjing univ sci...
3 篇 concordia univ d...
2 篇 shanghai jiao to...
2 篇 australian e-hea...
2 篇 telangana hydera...
2 篇 key lab data gov...
2 篇 idiap ch-1920 ma...
2 篇 department of ma...
2 篇 univ toronto dep...
2 篇 univ montreal di...
2 篇 chitkara univers...
2 篇 natl univ singap...
2 篇 pattern analysis...
2 篇 univ katolik ind...

作者

3 篇 li junbao
3 篇 maier andreas
3 篇 bengio s
3 篇 deepa r.
3 篇 deng fei
3 篇 pan jeng-shyang
3 篇 hu cong
3 篇 zhang daoqiang
3 篇 amelio alessia
3 篇 maier-hein klaus
3 篇 he chunhui
2 篇 vrahatis aristid...
2 篇 jin zhong
2 篇 collobert r
2 篇 tax dmj
2 篇 sharma r
2 篇 cilia nicole dal...
2 篇 fontanella franc...
2 篇 genov r
2 篇 stiefelhagen rai...

语言

568 篇 英文
12 篇 中文
9 篇 其他
1 篇 俄文

检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 585 条记录，以下是251-260 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：

Adaptive Joint Attention with Reinforcement Training for Convolutional Image Caption 1st

Adaptive Joint Attention with Reinforcement Training for C...

引用

1st international workshop on Human Brain and Artificial Intelligence, HBAI 2019, held in conjunction with the 28th international Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Chen, Ruoyu Li, Zhongnian Zhang, Daoqiang College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9789811513978

A convolutional decoder for image caption has proven to be easier to train than the Long Short Term Memory (LstM) decoder [2]. However, previous convolutional image captioning methods are not good at capture the relationship between generated words, which could lead to failure in predicting visually unrelated words. To address this issue, we propose an Adaptive Joint Attention (AJA) module for the convolutional decoder, by incorporating self-attention to explore the correlation between generated words automatically. Specifically, we develop a word gate with multi-head self-attention to directly capture words’ relationship. An adaptive combination strategy is proposed to learn which word is less related to image information. Besides, reinforcement learning is applied for directly optimizing the non-differentiable metrics while avoiding the exposure bias during inference. Extensive evaluations on two public data sets demonstrate that our method outperforms several state-of-the-art approaches in image captioning task. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Ensemble of 3D CNN Regressors with data Fusion for Fluid Intelligence Prediction 1

引用

1st Challenge in Adolescent Brain Cognitive Development Neurocognitive Prediction, ABCD-NP 2019, held in conjunction with the 22nd international Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019

作者： Pominova, Marina Kuzina, Anna Kondrateva, Ekaterina Sushchinskaya, Svetlana Burnaev, Evgeny Yarkin, Vyacheslav Sharaev, Maxim Skolkovo Institute of Science and Technology Moscow Russia

ISBN: (数字)9783030319014

ISBN: (纸本)9783030319007

In this work, we aimed at predicting children’s fluid intelligence scores based on structural T1-weighted MR images from the largest long-term study of brain development and child health. The target variable was regressed on a data collection site, sociodemographic variables, and brain volume, thus being independent to the potentially informative factors, which were not directly related to the brain functioning. We investigated both feature extraction and deep learning approaches as well as different deep CNN architectures and their ensembles. We proposed an advanced architecture of VoxCNNs ensemble, which yields MSE (92.838) on a blind test. © 2019, Springer Nature Switzerland AG.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Towards a practical process model for Anomaly Detection Systems 1

Towards a practical process model for Anomaly Detection Syst...

引用

1st ACM/IEEE international workshop on Software Engineering for Cognitive Services (SE4COG)

作者： Schwenzfeier, Nils Gruhn, Volker Univ Duisburg Essen Paluno Ruhr Inst Software Technol Duisburg Germany

ISBN: (纸本)9781450357401

Process models are an important tool for software engineers to produce reliable software within schedule and budget. Especially technically challenging domains like machine learning need a supportive process model to guide the developers and stakeholders during the development process. One major problem type of machine learning is anomaly detection. Its goal is to identify anomalous data points (outlier) between the normal data instances. Anomaly detection has a wide scope of applications in industrial and scientific areas. Detecting intruders in computer networks, distinguishing between cancerous and healthy tissue in medical images, cleaning data from disturbing outliers for further evaluation and many more. The cross-industry standard process for data mining (CRISP-DM) has been developed to support developers with all kinds of data mining applications. It describes a generic model of six phases that covers the whole development cycle. The generality of the CRISP-DM model is as much a strength as it is a weakness, since the particularities of different problem types like anomaly detection can not be addressed without making the model overly complex. There is a need for a more practical, specialised process model for anomaly detection applications. We demonstrate this issue and outline an approach towards a practical process model tailored to the development of anomaly detection systems.

关键词： Process model Anomaly detection machine learning data science CRISP-DM

来源：评论

学校读者我要写书评

暂无评论

Enhancing Person Re-identification based on Recurrent Feature Aggregation Network 1

Enhancing Person Re-identification based on Recurrent Featur...

引用

1st international Conference on Multimedia Analysis and pattern recognition (MAPR)

作者： Quan Nguyen Hong Thuy-Binh Nguyen Thi-Lan Le Hanoi Univ Sci & Technol Sch Elect & Commun Hanoi Vietnam Hanoi Univ Sci & Technol MICA Int Res Inst CNRS UMI2954 Grenoble INP Hanoi Vietnam Viet Hung Ind Univ Fac Informat Technol Hanoi Vietnam Univ Transport & Commun Fac Elect & Elect Engn Hanoi Vietnam

ISBN: (纸本)9781538641804

This paper proposes a method for video-based person re-identification. Motivated by the capacity of Recurrent Feature Aggregation Network (RFA-Net) that allows to aggregate the image-level features over time-steps and to yield a sequence-level feature. Our work improves the person re-identification performance by (1) proposing to use Gaussian of Gaussian (GOG) - a more powerful feature at image level instead of LBP-Color as in the original work and (2) applying metric learning techniques for person matching. To analyze the role of image feature and metric learning, we have performed extensive experiments on two public benchmark datasets including PRID 2011 and iLIDS-VID. The proposed solution obtains impressive results on the PRID 2011 dataset. The matching rate at rank 1 is increased by 16.8% compared to that of original method. For the iLIDS-VID dataset, the matching rate at rank 1 of proposed method is increased 1.1% whereas these values at the latter ranks are higher than those of the original method by approximately 4%. In additional, the mean and standard deviation of the rank where the correct matching is found of the proposed method on the PRID 2011 and the iLIDS-VID are 2.32, 4.33 and 4.61, 7.67 while those of the original work are 3.50, 5.42 and 8.03, 17.52, respectively. These obtained results emphasize the robustness of the proposed method compared to the original one.

关键词： Feature extraction Measurement Image color analysis Cameras Robustness data mining Image sequences image colour analysis image sequences Cameras Feature extraction data mining Persons Robustness image feature dataset Gauss Rank of data

来源：评论

学校读者我要写书评

暂无评论

Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functional MRI 1

引用

1st international workshop on Graph learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Yao, Dongren Liu, Mingxia Wang, Mingliang Lian, Chunfeng Wei, Jie Sun, Li Sui, Jing Shen, Dinggang Brainnetome Center & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel HillNC27599 United States College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health Ministry of Health Peking University Beijing100191 China

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

Brain functional connectivity (FC) derived from resting-state functional MRI (rs-fMRI) data has become a powerful approach to measure and map brain activity. Using fMRI data, graph convolutional network (GCN) has recently shown its superiority in learning discriminative representations of brain FC networks. However, existing studies typically utilize one specific template to partition the brain into multiple regions-of-interest (ROIs) for constructing FCs, which may limit the analysis to a single spatial scale (i.e., a fixed graph) determined by the template. Also, previous methods usually ignore the underlying high-order (e.g., triplet) association among subjects. To this end, we propose a multi-scale triplet graph convolutional network (MTGCN) for brain functional connectivity analysis with rs-fMRI data. Specifically, we first employ multi-scale templates for coarse-to-fine ROI parcellation to construct multi-scale FCs for each subject. We then develop a triplet GCN (TGCN) model to learn multi-scale graph representations of brain FC networks, followed by a weighted fusion scheme for classification. Experimental results on 1,218 subjects suggest the efficacy or our method. © 2019, Springer Nature Switzerland AG.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

Deleterious Effects of Uncertainty in Color Imagery streams on Classification Models

Deleterious Effects of Uncertainty in Color Imagery Streams ...

引用

international Conference on Artificial Intelligence and data Sciences (AiDAS)

作者： Syed Muslim Jameel Manzoor Ahmed Hashmani Hitham Al Hussain Mobashar Rehman Arif Budiman Department of Computer and Information Sciences Universiti Teknologi PETRONAS Sri Iskandar Malaysia Universiti Tunku Abdul Rahman Kampar Malaysia University of Indonesia Jakarta Indonesia

The following topics are dealt with: learning (artificial intelligence); feature extraction; pattern classification; support vector machines; text analysis; data mining; time series; image classification; optimisation... 详细信息

ISBN: (纸本)9781728130422

关键词： Uncertainty in classification machine learning Color imagery stream analytics Online classification on-line classification color image Integrated circuits program area streams rivers Uncertainty images Artificial Intelligence application task Taxonomy Imagery (Psychotherapy)

来源：评论

学校读者我要写书评

暂无评论

Graph Hyperalignment for Multi-subject fMRI Functional Alignment 1

引用

作者： Li, Weida Chen, Fang Zhang, Daoqiang College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

In fMRI analysis, the scientist seeks to aggregate multi-subject fMRI data so that inferences shared across subjects can be achieved. The challenge is to eliminate the variability of anatomical structure and functional topography of the human brain, which calls for aligning fMRI data across subjects. However, the existing methods do not exploit the geometry of the stimuli, which can be inferred by using certain domain knowledge and then serve as a priori. In this paper, such geometry is encoded in a graph matrix, and we propose an algorithm named Graph Hyperalignment for leveraging it. Specifically, a kernel-based optimization is developed to allow for non-linear feature extraction. To tackle overfitting caused by the high-spatial-and-low-temporal resolution of fMRI, the data in the new feature space are assumed to lie in a low-dimensional affine subspace, which can be implicitly integrated into the proposed optimization. Unlike other iterative existing methods, GHA reaches an optimal solution directly. Examining over four real datasets, Graph Hyperaligment achieves superior results to other methods. © 2019, Springer Nature Switzerland AG.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

Artificial Neural Networks Optimized with Unsupervised Clustering for IDS Classification

Artificial Neural Networks Optimized with Unsupervised Clust...

引用

international Conference on Smart Systems and data Science (ICSSD)

作者： Ichrak Lafram Naoual Berbiche Jamila El Alami Laboratoire d’Analyse des systèmes et Traitement d’Image et Management Mohammed V University Rabat Agdal Rabat Morocco

The following topics are dealt with: learning (artificial intelligence); data mining; pattern classification; Big data; production engineering computing; computer aided instruction; Internet; ontologies (artificial in... 详细信息

ISBN: (数字)9781728143682

ISBN: (纸本)9781728143699

关键词： Clustering algorithms Intrusion detection Support vector machines machine learning data models Classification algorithms Artificial neural networks

来源：评论

学校读者我要写书评

暂无评论

Improving Detection Accuracy for Imbalanced Network Intrusion Classification using Cluster-based Under-sampling with Random Forests

Improving Detection Accuracy for Imbalanced Network Intrusio...

引用

international Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)

作者： Md. Ochiuddin Miah Sakib Shahriar Khan Swakkhar Shatabda Dewan Md. Farid Department of Computer Science & Engineering United International University Dhaka Bangladesh

Network intrusion classification i n t he imbalanced big data environment becomes a significant and important issue in information and communications technology (ICT) in this digital era. Presently, intrusion detection systems (IDSs) are commonly using tool to detect and prevent internal and external network attacks/intrusions. IDSs are majorly bifurcated into host-based and network-based systems, and use pattern-matching techniques to detect intrusions that known as misuse-based intrusion detection system. machine learning (ML) and data mining (DM) algorithms are widely using for classifying intrusions in IDS over the last few decades. One of the major challenges for building IDS employing machine learning and data mining algorithms is to improve the intrusion classification accuracy and also reducing the false-positive rate. In this paper, we have introduced a new method for improving detection rate to classify minority-class network attacks/ intrusions using cluster-based under-sampling with Random Forest classifier. The proposed method is a multi-layer classification approach, which can process the highly imbalanced big data to correctly identify the minority/ rare class-intrusions. Initially, the proposed method classify a data point/ incoming data is attack/ intrusion or not (like normal behaviour), if it's an attack then the proposed method try to classify attack type and later sub-attack type. We have used cluster-based under-sampling technique to deal with class-imbalanced problem and popular ensemble classifier Random Forest for addressing overfitting problem. We have used KDD99 intrusion detection benchmark dataset for experimental analysis and tested the performance of proposed method with existing machine learning algorithms like: Artificial N eural Network (ANN), naïve Bayes (NB) classifier, Random Forest, and Bagging techniques.

关键词： Random forests Classification algorithms Intrusion detection Network intrusion machine learning algorithms Decision trees data mining

来源：评论

学校读者我要写书评

暂无评论

Traffic Sign recognition Based On Multi-feature Fusion and ELM Classifier 1

Traffic Sign Recognition Based On Multi-feature Fusion and E...

引用

1st international Conference on Intelligent Computing in data Sciences (ICDS)

作者： Aziz, Saouli Mohamed, El Aroussi Youssef, Fakhri Univ Ibn Tofail Fac Sci LaRIT Lab BP 242 Kenitra Morocco EHTP SIRC LAGES BP 8108 Casablanca Morocco

This paper proposes a novel and efficient method for traffic sign recognition based on combination of complementary and discriminative feature sets. The extracted features are the histogram of oriented gradients (HOG) feature, Gabor feature and Compound local binary pattern (CLBP) feature. The classification is performed using the extreme learning machine (ELM) algorithm. Performances of the proposed approach are evaluated on both German Traffic Sign recognition Benchmark (GTSRB) and Belgium Traffic Sign Classification (BTSC) datasets respectively. The results of the experimental work demonstrate that each feature yields fairly high accuracy and the combination of three features has shown good complementariness and yielded fast recognition rate and is more adequate for real-time application as well. (c) 2018 The Authors. Published by Elsevier B.V.

关键词： Traffic sign recognition Histogram of oriented gradients Compound Local Binary pattern Gabor filter Extreme learning machine

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59页 << < 22 23 24 25 26 27 28 29 30 31 > >>

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：