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

1,734 篇 会议
45 册 图书
12 篇 期刊文献

馆藏范围

1,790 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

1,229 篇 工学
- 1,097 篇 计算机科学与技术...
- 629 篇 软件工程
- 257 篇 信息与通信工程
- 201 篇 电气工程
- 177 篇 生物工程
- 102 篇 控制科学与工程
- 98 篇 生物医学工程（可授...
- 60 篇 光学工程
- 46 篇 电子科学与技术（可...
- 46 篇 化学工程与技术
- 42 篇 机械工程
- 41 篇 仪器科学与技术
- 40 篇 安全科学与工程
- 29 篇 动力工程及工程热...
- 29 篇 交通运输工程
- 27 篇 材料科学与工程（可...
457 篇 理学
- 200 篇 数学
- 197 篇 生物学
- 144 篇 物理学
- 84 篇 统计学（可授理学、...
- 51 篇 系统科学
- 46 篇 化学
237 篇 管理学
- 171 篇 图书情报与档案管...
- 89 篇 管理科学与工程(可...
- 46 篇 工商管理
182 篇 医学
- 160 篇 临床医学
- 72 篇 基础医学(可授医学...
- 39 篇 药学(可授医学、理...
- 32 篇 公共卫生与预防医...
34 篇 法学
- 30 篇 社会学
17 篇 经济学
14 篇 教育学
11 篇 农学
2 篇 艺术学
1 篇 军事学

主题

240 篇 machine learning
185 篇 deep learning
140 篇 pattern recognit...
110 篇 feature extracti...
91 篇 support vector m...
88 篇 data mining
79 篇 accuracy
75 篇 training
59 篇 convolutional ne...
51 篇 machine learning...
48 篇 data models
46 篇 artificial intel...
41 篇 computational mo...
40 篇 neural networks
39 篇 face recognition
36 篇 learning systems
36 篇 adversarial mach...
33 篇 predictive model...
32 篇 classification
32 篇 image recognitio...

机构

6 篇 college of compu...
5 篇 xi'an university...
5 篇 shanghai jiao to...
4 篇 badji mokhtar un...
4 篇 institute of big...
4 篇 chitkara centre ...
4 篇 chitkara univers...
4 篇 library shanghai...
3 篇 university of mi...
3 篇 school of comput...
3 篇 univ chittagong ...
3 篇 chongqing univer...
3 篇 centre of resear...
3 篇 school of comput...
3 篇 school of comput...
3 篇 school of design...
3 篇 korea univ dept ...
3 篇 school of automa...
3 篇 university of mi...
3 篇 shenyang jianzhu...

作者

5 篇 venkatesh svetha
4 篇 ashwini kodipall...
4 篇 zhang yan
4 篇 srinivasa gowri
4 篇 kamel mohamed s.
4 篇 lall brejesh
4 篇 trupthi rao
3 篇 andersson karl
3 篇 peng yang
3 篇 malerba donato
3 篇 karl andersson
3 篇 chaudhury santan...
3 篇 de-shuang huang
3 篇 sekhar c. chandr...
3 篇 wang shuo
3 篇 kamata sei-ichir...
3 篇 mohammad shahada...
3 篇 azizi nabiha
3 篇 phung dinh
3 篇 krasotkina olga

语言

1,515 篇 英文
267 篇 其他
26 篇 中文

检索条件"任意字段=4th International Conference on Machine Learning and Data Minining in Pattern Recognition"

共 1791 条记录，以下是1281-1290 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：

Clinical Named Entity recognition: Challenges and opportunities 4

Clinical Named Entity Recognition: Challenges and opportunit...

引用

4th IEEE international conference on Big data (Big data)

作者： Kundeti, Srinivasa Rao Vijayananda, J. Mujjiga, Srikanth Kalyan, M. Philips Healthcare Data Sci Bangalore Karnataka India

ISBN: (纸本)9781467390057

Information Extraction (IE), one of the important tasks in text analysis and Natural Language Processing (NLP), involves extracting meaningful pieces of knowledge from unstructured information sources, as unstructured data is computationally opaque. the intent of IE is to produce a knowledge base i.e. organize the information in a way that it is useful to people and arrange the information in a semantic way so that algorithms can make certain useful inferences from it. Named Entity recognition (NER) is a sub-task of IE which finds and classifies the names/entities. Once these Named Entities (NE) are extracted, they can then be indexed and made searchable, relations can be derived, questions can be answered and many more. NER techniques are different for different domains, because of the uniqueness that exists in each of the domains, although the process depends on a number of fundamental Natural Language Processing (NLP) steps such as tokenization, part-of-speech tagging, parsing and model building. As an example, NER in the medical domain involves handling of a number of vital tasks such as identification of medical terms, attributes such as negation, severity, identification of relationships between entities and mapping terms in the document to concepts in domain specific ontologies. there is also a heavy dependence on domain specific resources such as medical dictionaries and ontologies such as the Unified Medical Language System (UMLS)[34]. In this paper, we focus on NER in the clinical domain. In particular, we will focus on the NER challenges and the qualitative analysis of clinical reports on the approaches we took for the named entities: anatomies, findings, location qualifier, and procedures.

关键词： Natural Language Processing machine learning Text Analytics Deep learning Ontologies Language Modelling Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Hyperalignment of Multi-subject fMRI data by Synchronized Projections 4th

Hyperalignment of Multi-subject fMRI Data by Synchronized Pr...

引用

4th international Workshop on machine learning and Interpretation in Neuroimaging (MLINI) Held at conference on Neural Information Processing Systems (NIPS)

作者： Rustamov, Raif M. Guibas, Leonidas Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)9783319451732;9783319451749

Group analysis of fMRI data via multivariate pattern methods requires accurate alignments between neuronal activities of different subjects in order to attain competitive inter-subject classification rates. Hyperalignment, a recent technique pioneered by Haxby and collaborators, aligns the activations of different subjects by mapping them into a common abstract high-dimensional space. While hyperalignment is very successful in terms of classification performance, its "anatomy free" nature excludes the use of potentially helpful information inherent in anatomy. In this paper, we present a novel approach to hyperalignment that allows incorporating anatomical information in a non-trivial way. Experiments demonstrate the effectiveness of our approach over the original hyperalignment and several other natural alternatives.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Giving voice to office customers: Best practices in how office handles verbatim text feedback 4

Giving voice to office customers: Best practices in how offi...

引用

4th IEEE international conference on Big data, Big data 2016

作者： Bentley, Michael Batra, Soumya Office Product Group Microsoft RedmondWA United States

ISBN: (纸本)9781467390040

Microsoft Office users submit hundreds of thousands of pieces of verbatim feedback per month. How can an engineer or manager in Office find the signal in this data to make business decisions? this paper presents an overview of the Office Customer Voice (OCV) system. OCV combines classification, on-demand clustering and other machine learning techniques with a rich web UI to solve this problem. In this paper, we describe the different types of feedback received. Next, we outline the architecture used to build OCV. We then detail the text processing, classification and clustering done to reason on the data. Finally, we present challenges, future plans, and best practices that may be relevant to other teams analyzing customer feedback. We argue that this multi-pronged approach to handling customer feedback presents a pattern that other organizations can use to mature their handling of customer feedback. © 2016 IEEE.

关键词： learning systems

来源：评论

学校读者我要写书评

暂无评论

Deep learning in the Automotive Industry: Applications and Tools 4

Deep Learning in the Automotive Industry: Applications and T...

引用

4th IEEE international conference on Big data (Big data)

作者： Luckow, Andre Cook, Matthew Ashcraftt, Nathan Weill, Edwin Djerekarov, Emil Vorster, Bennie BMW Grp IT Res Ctr Informat Management Amer Greenville SC 29607 USA Clemson Univ Clemson SC USA Univ Cincinnati Cincinnati OH USA

ISBN: (纸本)9781467390057

Deep learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-theart in libraries, tools and infrastructures (e.g. GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.

关键词： Deep learning Cloud Computing Automotive Manufacturing

来源：评论

学校读者我要写书评

暂无评论

Reverse Engineering Smart card Malware using Side Channel Analysis with machine learning Techniques 4

Reverse Engineering Smart card Malware using Side Channel An...

引用

4th IEEE international conference on Big data (Big data)

作者： Tsague, Hippolyte Djonon Twala, Bheki CSIR MDS Smart Token Res Grp Pretoria South Africa Univ Johannesburg Dept Elect & Elect Engn Sci Inst Intelligent Syst Fac Engn Johannesburg South Africa

ISBN: (纸本)9781467390057

From inception, side channel leakage has been widely used for the purposes of extracting secret information, such as cryptographic keys, from embedded devices. However, in a few instances it has been utilized for extracting other information about the internal state of a computing device. In this paper, we exploit side channel information to recover large parts of the Sykipot malware program executed on a smart card. We present the first methodology to recover the program code of a smart card malware by evaluating its power consumption only. Besides well-studied methods from side channel analysis, we apply a combination of dimensionality reduction techniques in the form of PCA and LDA models to compress the large amount of data generated while preserving as much variance of the original data as possible. Among feature extraction techniques, PCA and LDA are very common dimensionality reduction algorithms that have successfully been applied in many classification problems like face recognition, character recognition, speech recognition, etc. with the chief objective being to eliminate insignificant data (without losing too much information) during the pre-processing step. In addition to quantifying the potential of the created side channel based disassembler, we highlight its diverse and unique application scenarios.

关键词： Side Channel Leakage Electromagnetic Templates Principal Components Analysis Linear Discriminant Analysis Multivariate Gaussian Distribution k-Nearest Neighbours Algorithm Reverse Engineering

来源：评论

学校读者我要写书评

暂无评论

Label-Alignment-Based Multi-Task Feature Selection for Multimodal Classification of Brain Disease 4th

Label-Alignment-Based Multi-Task Feature Selection for Multi...

引用

4th international Workshop on machine learning and Interpretation in Neuroimaging (MLINI) Held at conference on Neural Information Processing Systems (NIPS)

作者： Zu, Chen Jie, Biao Chen, Songcan Zhang, Daoqiang Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Engn Nanjing 210016 Jiangsu Peoples R China

ISBN: (纸本)9783319451732;9783319451749

Recently, multi-task feature selection methods have been applied to jointly identify the disease-related brain regions for fusing information from multiple modalities of neuroimaging data. However, most of those approaches ignore the complementary label information across modalities. To address this issue, in this paper, we present a novel label-alignment-based multi-task feature selection method to jointly select the most discriminative features from multi-modality data. Specifically, the feature selection procedure of each modality is treated as a task and a group sparsity regularizer (i.e., l(2),1 norm) is adopted to ensure that only a small number of features to be selected jointly. In addition, we introduce a new regularization term to preserve label relatedness. the function of the proposed regularization term is to align paired within-class subjects from multiple modalities, i.e., to minimize their distance in corresponding low-dimensional feature space. the experimental results on the magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data of Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed method can achieve better performances over state-of-the-art methods on multimodal classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI).

关键词： Alzheimer's disease Mild cognitive impairment Label alignment Multi-task learning Multi-modality

来源：评论

学校读者我要写书评

暂无评论

Non-deep CNN for Multi-Modal Image Classification and Feature learning: An Azure-based Model 4

Non-deep CNN for Multi-Modal Image Classification and Featur...

引用

4th IEEE international conference on Big data (Big data)

作者： Roychowdhury, Sohini Ren, Johnny Univ Washington Dept Elect & Comp Engn Bothell WA 98011 USA

ISBN: (纸本)9781467390057

Convolutional Neural Networks (CNN) are useful methods for identification of previously unknown embedded patterns in images. Several object and facial recognition along with image segmentation tasks have benefited from the non-linear abstraction of hybrid features using CNN. this work presents a novel CNN model parametrization work-flow developed on the cloud-computing platform of Microsoft Azure machine learning Studio (MAMLS) that is capable of learning from the feature maps and classifying multi-modal images with different variabilities using one common flow. this two-step work-flow trains CNN models using 70/ 30 data split. First, the CNN layers are fixed and the optimal kernel and normalization parameters are identified that maximize classification accuracy on the test data. Next, using the optimal kernel and normalization parameters, the best CNN architecture that maximizes classification accuracy is detected. Finally, the activated feature maps (AFMs) from the optimally parameterized CNN model are analyzed to learn new features that can enhance image-based classification accuracies. the proposed flow achieves classification accuracies in the range of 92.5-99.2% that can be further enhanced by doubling the samples based on the features learned from the AFMs. the proposed non-deep CNN models in the MAMLS platform are capable of processing image data sets with 400-4 million samples using a common flow without exponential increase in the computation time. thus, optimally parametrized non-deep CNN models are capable of identifying novel features that may enhance imagebased classification accuracies.

关键词： Microsoft Azure Convolutional neural network CT image architecture

来源：评论

学校读者我要写书评

暂无评论

Automated Quality Control for Proton Magnetic Resonance Spectroscopy data Using Convex Non-negative Matrix Factorization 4th

Automated Quality Control for Proton Magnetic Resonance Spec...

引用

4th international Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Mocioiu, Victor Kyathanahally, Sreenath P. Arus, Carles Vellido, Alfredo Julia-Sape, Margarida Univ Autonoma Barcelona Dept Bioquim & Biol Mol E-08193 Barcelona Spain Univ Bern Dept Radiol & Clin Res Bern Switzerland Univ Politecn Cataluna BarcelonaTech Dept Ciencies Comp Campus Nord ES-08034 Barcelona Spain Ctr Invest Biomed Red Bioingn Biomat & Nanomed CI Barcelona Spain

ISBN: (纸本)9783319317441;9783319317434

Proton Magnetic Resonance Spectroscopy (H-1 MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex Non-Negative Matrix Factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.

关键词： Brain tumors Magnetic resonance spectroscopy Convex non-negative matrix factorization pattern recognition Quality control machine learning

来源：评论

学校读者我要写书评

暂无评论

4th international conference on Distributed, Ambient and Pervasive Interactions, DAPI 2016 held as part of 18th international conference on Human-Computer Interaction, HCI international 2016

4th International Conference on Distributed, Ambient and Per...

引用

18th international conference on Human-Computer Interaction, HCI international 2016

ISBN: (纸本)9783319398617

the proceedings contain 45 papers. the special focus in this conference is on Developing Smart Environments, recognition Techniques in Ambient Intelligence, Tracking, Human Behavior in Smart Environments and Affect in Intelligent Environments. the topics include: Towards ubiquitous services design and development approach;exploring design for multi-device, multi-environment and multimodal connected experiences;investigating low-cost wireless occupancy sensors for beds;user interface design for ambient assisted living systems;establishing guidelines for user quality of experience in ubiquitous systems;towards big data interactive visualization in ambient intelligence environments;end-user development tools for the smart home;the interaction design research about 3d demo animation in smart home;a formal model for context-aware semantic augmented reality systems;memory and learning neural circuits correlated with the creative processes in design;data-driven smart home system for elderly people based on web technologies;a unified framework for remote collaboration using interactive AR authoring and hands tracking;exploring machine learning object classification for interactive proximity surfaces;machine learning and location fingerprinting to improve UX in a ubiquitous application;exploring the ergonomic issues of user-defined mid-air gestures for interactive product exhibition;facial tracking-assisted hand pointing technique for wall-sized displays;user-independent face landmark detection and tracking for spatial AR interaction;mid-air gestures for virtual modeling with leap motion and mental model development using collaborative 3d virtual environments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

OpenCL caffe: Accelerating and enabling a cross platform machine learning framework 16

OpenCL caffe: Accelerating and enabling a cross platform mac...

引用

4th international Workshop on OpenCL, IWOCL 2016

作者： Gu, Junli Liu, Yibing Gao, Yuan Zhu, Maohua

ISBN: (纸本)9781450343381

Deep neural networks (DNN) achieved significant breakthrough in vision recognition in 2012 and quickly became the leading machine learning algorithm in Big data based large scale object recognition applications. the successful deployment of DNN based applications pose challenges for a cross platform software framework that enable multiple user scenarios, including offline model training on HPC clusters and online recognition in embedded environments. Existing DNN frameworks are mostly focused on a closed format CUDA implementations, which is limiting of deploy breadth of DNN hardware systems. this paper presents OpenCL™ caffe, which targets in transforming the popular CUDA based framework caffe [1] into open standard OpenCL backend. the goal is to enable a heterogeneous platform compatible DNN framework and achieve competitive performance based on OpenCL tool chain. Due to DNN models' high complexity, we use a two-phase strategy. First we introduce the OpenCL porting strategies that guarantee algorithm convergence;then we analyze OpenCL's performance bottlenecks in DNN domain and propose a few optimization techniques including batched manner data layout and multiple command queues to better map the problem size into existing BLAS library, improve hardware resources utilization and boost OpenCL runtime efficiency. We verify OpenCL caffe's successful offline training and online recognition on both server-end and consumer-end GPUs. Experimental results show that the phase-two's optimized OpenCL caffe achieved a 4.5x speedup without modifying BLAS library. the user can directly run mainstream DNN models and achieves the best performance for a specific processors by choosing the optimal batch number depending on H/W properties and input data size.

关键词： Deep neural networks

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180页 << < 125 126 127 128 129 130 131 132 133 134 > >>

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：