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

476 篇 期刊文献
310 篇 会议
4 篇 学位论文

馆藏范围

790 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

736 篇 工学
- 491 篇 计算机科学与技术...
- 301 篇 电气工程
- 129 篇 信息与通信工程
- 98 篇 软件工程
- 64 篇 控制科学与工程
- 48 篇 仪器科学与技术
- 31 篇 电子科学与技术（可...
- 26 篇 机械工程
- 26 篇 石油与天然气工程
- 22 篇 生物医学工程（可授...
- 14 篇 测绘科学与技术
- 12 篇 动力工程及工程热...
- 10 篇 交通运输工程
- 9 篇 材料科学与工程（可...
- 9 篇 环境科学与工程（可...
- 9 篇 网络空间安全
- 7 篇 光学工程
- 7 篇 化学工程与技术
- 5 篇 土木工程
129 篇 理学
- 51 篇 物理学
- 38 篇 生物学
- 29 篇 化学
- 23 篇 数学
- 10 篇 地球物理学
- 5 篇 系统科学
90 篇 医学
- 63 篇 临床医学
- 24 篇 基础医学(可授医学...
- 14 篇 特种医学
59 篇 管理学
- 51 篇 管理科学与工程(可...
- 8 篇 图书情报与档案管...
6 篇 农学
3 篇 法学
2 篇 经济学
2 篇 教育学
1 篇 文学
1 篇 艺术学

主题

790 篇 auto-encoder
193 篇 deep learning
51 篇 feature extracti...
48 篇 anomaly detectio...
43 篇 machine learning
38 篇 unsupervised lea...
26 篇 convolutional ne...
25 篇 lstm
21 篇 neural networks
21 篇 classification
19 篇 task analysis
19 篇 deep neural netw...
19 篇 convolutional ne...
18 篇 neural network
16 篇 cnn
16 篇 training
15 篇 fault diagnosis
15 篇 extreme learning...
14 篇 clustering
14 篇 generative adver...

机构

6 篇 fuzhou univ coll...
5 篇 northeastern uni...
5 篇 univ elect sci &...
4 篇 jiangnan univ sc...
4 篇 sichuan univ col...
4 篇 yunnan univ sch ...
4 篇 air force engn u...
4 篇 northeastern uni...
4 篇 southeast univ s...
3 篇 china univ min &...
3 篇 nanjing univ sta...
3 篇 xidian univ sch ...
3 篇 tianjin univ col...
3 篇 jiangnan univ sc...
3 篇 fudan univ acad ...
3 篇 univ elect sci &...
3 篇 north china elec...
3 篇 univ sci & techn...
3 篇 shanghai jiao to...
3 篇 huazhong univ sc...

作者

6 篇 wang shiping
5 篇 liu yang
5 篇 fu yun
5 篇 zhou lihua
4 篇 cai jinyu
4 篇 li rui
4 篇 zhang wanqing
4 篇 wang xiaodan
4 篇 li shuaichao
4 篇 lei lei
4 篇 guo wenzhong
4 篇 cui ying
3 篇 xie xijiong
3 篇 fu r.
3 篇 fayyazi hossein
3 篇 zuo wenbo
3 篇 li yanfeng
3 篇 liu zhen
3 篇 wang li
3 篇 liu jing

语言

759 篇 英文
18 篇 其他
4 篇 中文
1 篇 德文
1 篇 朝鲜文

检索条件"主题词=Auto-Encoder"

共 790 条记录，以下是701-710 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：

POLARIZATION FEATURE EXTRACTION USING QUATERNION NEURAL NETWORKS FOR FLEXIBLE UNSUPERVISED POLSAR LAND CLASSIFICATION 38

POLARIZATION FEATURE EXTRACTION USING QUATERNION NEURAL NETW...

引用

38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Kim, Hyunsoo Hirose, Akira Univ Tokyo Dept Elect Engn & Informat Syst Tokyo 1138656 Japan

ISBN: (纸本)9781538671504

We propose an unsupervised PolSAR land classification system consisting of quaternion auto-encoder and quaternion self-organizing map (SOM). Most of the conventional methods extract features necessary for the land classification based on a few of scattering models predefined by human beings. However, we can not expect classification into a large number of land categories recognizable to humans by using such restricted features. In this paper, we propose a method employing quaternion auto-encoder and quaternion SOM for feature extraction and classification, respectively. As a result, we succeed in discovering new and more detailed land categories. For example, town areas are divided into residential areas and factory sites.

关键词： Polarimetric synthetic aperture radar (PolSAR) land classification quaternion neural network unsupervised classification Poincare parameter auto-encoder self-organizing map (SOM)

来源：评论

学校读者我要写书评

暂无评论

autoCOT - autoencoder Based Cooperative Training For Sparse Recommendation 15

AutoCOT - AutoEncoder Based Cooperative Training For Sparse ...

引用

IEEE 15th International Conference on E-Business Engineering (ICEBE)

作者： Bai, Rong Zhu, Haiping Ni, Yifu Chen, Yan Zheng, Qinghua Xi An Jiao Tong Univ Dept Comp Sci & Technol MOEKLINNS Lab Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Dept Comp Sci & Technol MOEKLINNS Lab Sch Elect & Informat Engn Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Dept Comp Sci & Technol Shaanxi Prov Key Lab Satellite & Terr Network Tec Xian Shaanxi Peoples R China

ISBN: (纸本)9781538679920

Currently, data sparseness problem caused by large amount of data has resulted in low recommendation quality of traditional recommendation algorithms. Aiming at this problem, this paper proposes an auto-encoder recommendation algorithm based on cooperative training (autoCOT) that combines the auto-encoder framework with cooperative training (COT) model, which can not only better learn the non-linear relationship of data but alleviate the data sparseness problem, especially in large amount of user and item data. The experiments show that, on Movielen datasets, autoCOT performs better in coverage, precision and recall rate when compares with the traditional collaborative filtering algorithms and pure auto-encoder recommendation algorithms.

关键词： auto-encoder COT Recommendation Collaborative filtering Data sparseness

来源：评论

学校读者我要写书评

暂无评论

AutLoc: Deep autoencoder for Indoor Localization with RSS Fingerprinting 10

AutLoc: Deep Autoencoder for Indoor Localization with RSS Fi...

引用

10th International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Liu, Jing Liu, Nan Pan, Zhiwen You, Xiaohu Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781538661192

Wi-Fi based indoor localization has attracted great interest due to its ubiquitous access in many indoor environments. However, the accuracy is deteriorated by the complex indoor propagation environments, which result in variable received signal strength (RSS). In this paper, we propose to utilize an autoencoder to improve the accuracy of indoor localization by preprocessing the noisy RSS. The AutLoc system includes an offline training phase and an online localization phase. In the offline training phase, we train the deep autoencoder to denoise the measured data and then build the RSS fingerprints according to the trained weights. In the online localization phase, we adopt three machine learning algorithms, which are random forest regression, multi-player perceptron classification and multi-player perceptron regression, to estimate the location. Averaging over the results of three algorithms, we then obtain the final estimated location. Simulation results justify superiority of the proposed AutLoc system over previous indoor location schemes in vast scenarios.

关键词： WLAN indoor localization fingerprinting auto-encoder RSS noise reduction

来源：评论

学校读者我要写书评

暂无评论

Partition-Aware Scalable Outlier Detection Using Unsupervised Learning 19

Partition-Aware Scalable Outlier Detection Using Unsupervise...

引用

IEEE 19th International Conference on Information Reuse and Integration for Data Science (IRI)

作者： Parveen, Pallabi Lee, Melissa Henslee, Austin Dugan, Matt Ford, Brad AT&T Chief Data Off Richardson TX USA

ISBN: (纸本)9781538626597

Anomaly Detection is an important area of research in data science. However, for big data it is challenging due to limited (or no) labeled data during training. This challenge is prevalent in the cyber-security area. There is always a lag before labels of (near) real time data become available. Hence, we would like to develop a new anomaly detection framework without any labeled data (no human intervention). For this, we utilize the density-based spatial clustering of applications with noise (DBSCAN). To address the scalability issues that arise when using DBSCAN, we exploit novel techniques based on partitions with minimum false alarm. We compare our approaches with other unsupervised approaches to demonstrate the effectiveness of our work.

关键词： Clustering Outlier Unsupervised Learning Support Vector Machine auto-encoder

来源：评论

学校读者我要写书评

暂无评论

Real-time Anomaly Detection with HMOF Feature 18

Real-time Anomaly Detection with HMOF Feature

引用

2nd International Conference on Video and Image Processing (ICVIP)

作者： Zhu, Huihui Liu, Bin Lu, Yan Li, Weihai Yu, Nenghai Univ Sci & Technol China Sch Informat Sci & Technol Hefei Anhui Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Beijing Peoples R China

ISBN: (纸本)9781450366137

Anomaly detection is a challenging problem in intelligent video surveillance. Most existing methods are computation-consuming, which cannot satisfy the real-time requirement. In this paper, we propose a real-time anomaly detection framework with low computational complexity and high efficiency. A new feature descriptor, named Histogram of Magnitude Optical Flow (HMOF), is proposed to capture the motion of video patches. Compared with existing feature descriptors, HMOF is more sensitive to motion magnitude and more efficient to distinguish anomaly information. The HMOF features are computed for foreground patches, and are reconstructed by the auto-encoder for better clustering. Then, we use Gaussian Mixture Model (GMM) Classifiers to distinguish anomalies from normal activities in videos. Besides, visual tracking can be adopted in our algorithm for better performance. Experimental results show that our framework outperforms state-of-the-art methods, and can reliably detect anomalies in real-time.

关键词： Anomaly detection HMOF auto-encoder Real-time Tracking

来源：评论

学校读者我要写书评

暂无评论

SalNet: Edge Constraint Based End-to-End Model for Salient Object Detection 1st

SalNet: Edge Constraint Based End-to-End Model for Salient O...

引用

1st Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Han, Le Li, Xuelong Dong, Yongsheng Chinese Acad Sci Xian Inst Opt & Precis Mech Ctr OpT IMagery Anal & Learning OPTIMAL Xian 710119 Shaanxi Peoples R China Univ Chinese Acad Sci 19A Yuquanlu Beijing 100049 Peoples R China

ISBN: (纸本)9783030033415;9783030033408

Salient object detection is a fundamental task in computer vision and pattern recognition. And it has been investigated by many researchers in many fields for a long time. Numerous salient object detection models based on deep learning have been designed in recent years. However, the saliency maps extracted by most of the existing models are blurry or have irregular edges. To alleviate these problems, we propose a novel approach named SalNet to detect the salient objects accurately in this paper. The architecture of the SalNet is an U-Net which can combine the features of the shallow and deep layers. Moreover, a new objective function based on the image convolution is further proposed to refine the edges of saliency maps by using a constraint on the L1 distance between edge information of the ground-truth and the saliency maps. Finally, we evaluate our proposed SalNet on benchmark datasets and compare it with the state-of-the-art algorithms. Experimental results demonstrate that the SalNet is effective and outperforms several representative methods in salient object detection task.

关键词： Salient object detection U-Net auto-encoder Image convolution

来源：评论

学校读者我要写书评

暂无评论

Deep-Learning Based Facial Expression Recognition System Evaluated on Three Spontaneous Databases 9

Deep-Learning Based Facial Expression Recognition System Eva...

引用

9th International Symposium on Signal, Image, Video and Communications (ISIVC)

作者： Hussein, Hayfaa Angelini, Federico Naqvi, Mohsen Chambers, Jonathon A. Newcastle Univ Sch Engn ISC Res Grp Newcastle Upon Tyne NE1 7RU Tyne & Wear England

ISBN: (纸本)9781538681732

Feature extraction and selection are significant operations to improve the recognition accuracy of facial expression systems. The distribution of geometric features and their quantity plays a decisive role in the quality of the process of image matching, particularly for some databases which have more challenges in terms of system accuracy. In this paper, we exploit a robust system to mitigate these challenges as this is essential for real-time applications. We concentrate on geometric feature extraction automatically from raw data with one of the most attractive methods for classification in the field of neural networks namely deep learning. Our improved system consists of the following: solving the misalignment problem of the training images, lower complexity for geometric feature extraction, and finally, auto-encoder deep learning. The performance of the image-based expression recognition is evaluated for the first time on three spontaneous databases with different challenges with geometric and appearance based features for comparison. The three spontaneous databases are the Video Database of Moving Faces and People (VDMFP), MMI facial expression database and Belfast Induced Natural Emotion Database (BINED) each having different challenges in terms of system accuracy. Deep learning with a high-level feature representation, clearly outperforms state-of-the-art techniques.

关键词： Feature extraction face alignment geometric features appearance features auto-encoder deep learning Self-Organizing Map (SOM)

来源：评论

学校读者我要写书评

暂无评论

CODEBOOK-BASED HIERARCHICAL POLARIZATION FEATURE FOR UNSUPERVISED FINE LAND CLASSIFICATION USING HIGH-RESOLUTION POLSAR DATA 38

CODEBOOK-BASED HIERARCHICAL POLARIZATION FEATURE FOR UNSUPER...

引用

38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Kim, Hyunsoo Hirose, Akira Univ Tokyo Dept Elect Engn & Informat Syst Tokyo 1138656 Japan

ISBN: (纸本)9781538671504

We propose a codebook-based hierarchical polarization feature vector generation to realize an unsupervised land classification with high-resolution PolSAR data. PolSAR has reached the high-resolution of decimeter level. Conventional methods perform the spatial averaging in 10m to 20m square real-space area to classify observed land into categories such as farm, forest, and town. However, with this averaging, we can not expect to discover new detailed land classes by resolution improvement, since the resolution of the PolSAR data is lowered in the averaging process. Our proposal in this paper generates feature vectors useful for classifying the land pieces into categories while preserving the detailed polarization features in respective pixels of the high-resolution PolSAR data. Then, the method discovers the new detailed land classes that can be available only in the high-resolution PolSAR data.

关键词： Polarimetric synthetic aperture radar (PolSAR) unsupervised land classification high-resolution hierarchical polarization feature quaternion neural networks Poincare parameter auto-encoder self-organizing map (SOM)

来源：评论

学校读者我要写书评

暂无评论

Feature Learning and Classification in Neuroimaging: Predicting Cognitive Impairment from Magnetic Resonance Imaging 4

Feature Learning and Classification in Neuroimaging: Predict...

引用

4th International Conference on Big Data and Information Analytics (BigDIA)

作者： Shi, Shan Nathoo, Farouk Univ Victoria Dept Math & Stat Victoria BC Canada

ISBN: (纸本)9781538668887

Due to the rapid innovation of technology and the desire to find and employ biomarkers for neurodegenerative disease, high-dimensional data classification problems are routinely encountered in neuroimaging studies. To avoid over-fitting and to explore relationships between disease and potential biomarkers, feature learning and selection plays an important role in classifier construction and is an important area in machine learning. In this article, we review several important feature learning and selection techniques including lasso-based methods, PCA, the two-sample t-test, and stacked auto-encoders. We compare these approaches using a numerical study involving the prediction of Alzheimer's disease from Magnetic Resonance Imaging.

关键词： auto-encoder Classification Dimensionality Reduction Feature Selection High Dimensionality Magnetic Resonance Imaging Penalized Least Squares

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Approach To Detect Code Smells using Deep Learning 13

A Hybrid Approach To Detect Code Smells using Deep Learning

引用

13th International Conference on Evaluation of Novel Approaches to Software Engineering

作者： Hadj-Kacem, Mouna Bouassida, Nadia Sfax Univ Mir Cl Lab Sfax Tunisia

ISBN: (纸本)9789897583001

The detection of code smells is a fundamental prerequisite for guiding the subsequent steps in the refactoring process. The more the detection results are accurate, the more the performance of the refactoring on the software is improved. Given its influential role in the software maintenance, this challenging research topic has so far attracted an increasing interest. However, the lack of consensus about the definition of code smells in the literature has led to a considerable diversity of the existing results. To reduce the confusion associated with this lack of consensus, there is a real need to achieve a deep and consistent representation of the code smells. Recently, the advance of deep learning has demonstrated an undeniable contribution in many research fields including the pattern recognition issues. In this paper, we propose a hybrid detection approach based on deep auto-encoder and Artificial Neural Network algorithms. Four code smells (God Class, Data Class, Feature Envy and Long Method) are the focus of our experiment on four adopted datasets that are extracted from 74 open source systems. The values of recall and precision measurements have demonstrated high accuracy results.

关键词： Hybrid Approach Deep Learning auto-encoder Artificial Neural Networks Code Smell Detection

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79页 << < 67 68 69 70 71 72 73 74 75 76 > >>

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：