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

248 篇 会议
18 篇 期刊文献
8 册 图书

馆藏范围

274 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

231 篇 工学
- 217 篇 计算机科学与技术...
- 53 篇 软件工程
- 32 篇 信息与通信工程
- 15 篇 生物工程
- 13 篇 电气工程
- 10 篇 生物医学工程（可授...
- 6 篇 光学工程
- 4 篇 控制科学与工程
- 4 篇 化学工程与技术
- 3 篇 机械工程
- 3 篇 材料科学与工程（可...
- 3 篇 建筑学
- 2 篇 土木工程
- 1 篇 仪器科学与技术
- 1 篇 电子科学与技术（可...
- 1 篇 石油与天然气工程
63 篇 理学
- 37 篇 数学
- 21 篇 生物学
- 15 篇 物理学
- 8 篇 统计学（可授理学、...
- 3 篇 化学
- 2 篇 系统科学
24 篇 管理学
- 20 篇 图书情报与档案管...
- 4 篇 管理科学与工程(可...
- 3 篇 工商管理
11 篇 医学
- 10 篇 临床医学
- 3 篇 基础医学(可授医学...
- 3 篇 药学(可授医学、理...
4 篇 法学
- 4 篇 社会学
1 篇 经济学
- 1 篇 应用经济学
1 篇 农学

主题

22 篇 neural networks
19 篇 pattern recognit...
14 篇 deep learning
11 篇 convolutional ne...
10 篇 artificial intel...
7 篇 face recognition
7 篇 feature extracti...
7 篇 supervised learn...
7 篇 recurrent neural...
5 篇 deep neural netw...
5 篇 recurrent neural...
5 篇 emotion recognit...
4 篇 neurons
4 篇 support vector m...
4 篇 human computer i...
4 篇 image segmentati...
4 篇 data mining and ...
4 篇 graph neural net...
4 篇 density estimati...
4 篇 recursive neural...

机构

11 篇 univ ulm inst ne...
5 篇 concordia univ d...
5 篇 univ ulm dept ne...
4 篇 institute of neu...
4 篇 ulm univ inst ne...
4 篇 univ siena dipar...
3 篇 concordia univ d...
3 篇 univ siena dipar...
2 篇 univ london birk...
2 篇 tech univ claust...
2 篇 univ jaume 1 dep...
2 篇 univ siena siena
2 篇 institute of neu...
2 篇 institute for in...
2 篇 helsinki univ te...
2 篇 univ paris 06 la...
2 篇 ulm university i...
2 篇 heriot watt univ...
2 篇 univ ulm inst ne...
2 篇 univ hosp ulm de...

作者

41 篇 schwenker friedh...
12 篇 trentin edmondo
10 篇 palm guenther
8 篇 kestler hans a.
8 篇 meudt sascha
6 篇 el gayar neamat
6 篇 riesen kaspar
6 篇 thiam patrick
5 篇 stadelmann thilo
5 篇 kaechele markus
5 篇 amirian mohammad...
4 篇 burnaev evgeny
4 篇 hernandez-espino...
4 篇 hammer barbara
4 篇 lausser ludwig
4 篇 bianchini monica
4 篇 friedhelm schwen...
4 篇 fernandez-redond...
4 篇 torres-sospedra ...
4 篇 fischer andreas

语言

273 篇 英文
1 篇 中文

检索条件"任意字段=3rd IAPR Workshop on Artificial Neural Networks in Pattern Recognition"

共 274 条记录，以下是81-90 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：

A Refinement Algorithm for Deep Learning via Error-Driven Propagation of Target Outputs 8th

A Refinement Algorithm for Deep Learning via Error-Driven Pr...

引用

8th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Laveglia, Vincenzo Trentin, Edmondo Univ Firenze DINFO Via S Marta 3 I-50139 Florence Italy Univ Siena DIISM Via Roma 56 I-53100 Siena Italy

ISBN: (纸本)9783319999784;9783319999777

Target propagation in deep neural networks aims at improving the learning process by determining target outputs for the hidden layers of the network. To date, this has been accomplished via gradient-descent or relying on autoassociative networks applied top-to-bottom in order to synthesize targets at any given layer from the targets available at the adjacent upper layer. This paper proposes a different, error-driven approach, where a regular feed-forward neural net is trained to estimate the relation between the targets at layer l and those at layer l - 1 given the error observed at layer l. The resulting algorithm is then combined with a pre-training phase based on backpropagation, realizing a proficuous "refinement" strategy. Results on the MNIST database validate the feasibility of the approach.

关键词： Target propagation Deep learning Deep neural network Refinement learning

来源：评论

学校读者我要写书评

暂无评论

Global Coordination Based on Matrix neural Gas for Dynamic Texture Synthesis

Global Coordination Based on Matrix Neural Gas for Dynamic T...

引用

4th workshop on artificial neural networks in pattern recognition

作者： Arnonkijpanich, Banchar Hammer, Barbara Khon Kaen Univ Fac Sci Dept Math Khon Kaen 40002 Thailand Univ Bielefeld CITEC D-33615 Bielefeld Germany Commiss Higher Educ Ctr Excellence Math Bangkok 10400 Thailand

ISBN: (纸本)9783642121586

Matrix neural gas has been proposed as a mathematically well-founded extension of neural gas networks to represent data in terms of prototypes and local principal components in a smooth way. The additional information provided by local principal directions can directly be combined with charting techniques such that a nonlinear embedding of a data manifold into low dimensions results for which an explicit function as well as an approximate inverse exists. In tins paper, we show that these ingredients can be used to embed dynamic textures in low dimensional spaces such that, together with a traversing technique in the low dimensional representation, efficient dynamic texture synthesis can be obtained.

关键词： Matrix algebra

来源：评论

学校读者我要写书评

暂无评论

ATM Protection Using Embedded Deep Learning Solutions 8th

ATM Protection Using Embedded Deep Learning Solutions

引用

8th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Rossi, Alessandro Rizzo, Antonio Montefoschi, Francesco Univ Siena Siena Italy

ISBN: (纸本)9783319999784;9783319999777

Last decade advances in Deep Learning methods lead to sensible improvements in state of the art results in many real world applications, thanks to the exploitation of particular artificial neural networks architectures. In this paper we present an investigation of the application of such kind of structures to a Video Surveillance case of study, in which the special nature and the small amount of available data increases the difficulties during the training phase. The analyzed scenario involves the protection of Automatic Teller Machines (ATM), representing a sensitive problem in the world of both banking and public security. Because of the critical issues related to this environment, even apparently small improvements in either accuracy or responsiveness of surveillance systems can produce a fundamental contribution. Even if the experimentation has been reproduced in an artificial scenario, the results show that the implemented architecture is able to classify depth data in real-time on an embedded system, detecting all the test attacks in a few seconds.

关键词： Deep Learning Convolutional neural networks Recurrent neural networks Computer Vision Bank security Embedded systems Depth images

来源：评论

学校读者我要写书评

暂无评论

A study of the robustness of KNN classifiers trained using soft labels

引用

2nd iapr workshop on artificial neural networks in pattern recognition

作者： El Gayar, Neamat Schwenker, Friedhelm Palm, Guenther Cairo Univ Fac Comp & Informat Giza 12613 Egypt Univ Ulm Dept Neural Informat Proc D-89069 Ulm Germany

ISBN: (纸本)3540379517

Supervised learning models most commonly use crisp labels for classifier training. Crisp labels fail to capture the data characteristics when overlapping classes exist. In this work we attempt to compare between learning using soft and hard labels to train K-nearest neighbor classifiers. We propose a new technique to generate soft labels based on fuzzy-clustering of the data and fuzzy relabelling of cluster prototypes. Experiments were conducted on five data sets to compare between classifiers that learn using different types of soft labels and classifiers that learn with crisp labels. Results reveal that learning with soft labels is more robust against label errors opposed to learning with crisp labels. The proposed technique to find soft labels from the data, was also found to lead to a more robust training in most data sets investigated.

关键词： Data structures

来源：评论

学校读者我要写书评

暂无评论

Sign-based learning schemes for pattern classification

Sign-based learning schemes for pattern classification

引用

1st iapr TC3 workshop on artificial neural networks in pattern recognition

作者： Anastasiadis, AD Magoulas, GD Vrahatis, MN Univ London Birkbeck Coll Sch Comp Sci & Informat Syst Knowledge Lab London WC1N 3QS England Univ London Birkbeck Coll Sch Comp Sci & Informat Syst London WC1E 7HX England Univ Patras UPAIRC Dept Math GR-26110 Patras Greece

This paper introduces a new class of sign-based training algorithms for neural networks that combine the sign-based updates of the Rprop algorithm with the composite nonlinear Jacobi method. The theoretical foundations of the class are described and a heuristic Rprop-based Jacobi algorithm is empirically investigated through simulation experiments in benchmark pattern classification problems. Numerical evidence shows that this new modification of the Rprop algorithm exhibits improved learning speed in all cases tested, and compares favorably against the Rprop and a recently proposed modification, the improved Rprop. (c) 2005 Elsevier B.V. All rights reserved.

关键词： supervised learning nonlinear iterative methods nonlinear Jacobi subminimization feed-forward neural networks Rprop pattern classification

来源：评论

学校读者我要写书评

暂无评论

Maximum-Likelihood Estimation of neural Mixture Densities: Model, Algorithm, and Preliminary Experimental Evaluation 8th

Maximum-Likelihood Estimation of Neural Mixture Densities: M...

引用

8th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Trentin, Edmondo Univ Siena Dipartimento Ingn Informaz & Sci Matemat Siena Italy

ISBN: (纸本)9783319999784;9783319999777

Unsupervised estimation of probability density functions by means of parametric mixture densities (e.g., Gaussian mixture models) may improve significantly over plain, single-density estimators in terms of modeling capabilities. Moreover, mixture densities (and even mixtures of mixture densities) may be exploited for the statistical description of phenomena whose data distributions implicitly depend on the distinct outcomes of a number of non-observable, latent states of nature. In spite of some recent advances in density estimation via neural networks, no proper mixtures of neural component densities have been investigated so far. The paper proposes a first algorithm for estimating neural Mixture Densities based on the usual maximum-likelihood criterion, satisfying numerically a combination of hard and soft constraints aimed at ensuring a proper probabilistic interpretation of the resulting model. Preliminary results are presented and their statistical significance is assessed, corroborating the soundness of the approach with respect to established statistical techniques.

关键词： Density estimation Mixture density Unsupervised learning Constrained learning Mixture of experts

来源：评论

学校读者我要写书评

暂无评论

Unsupervised feature selection for biomarker identification in chromatography and gene expression data

引用

2nd iapr workshop on artificial neural networks in pattern recognition

作者： Strickert, Marc Sreenivasulu, Nese Peterek, Silke Weschke, Winfriede Mock, Hans-Peter Seiffert, Udo Pattern Recognition Group Leibniz Institute of Plant Genetics and Crop Plant Research Gatersleben Germany Gene Expression Group Leibniz Institute of Plant Genetics and Crop Plant Research Gatersleben Germany Applied Biochemistry Leibniz Institute of Plant Genetics and Crop Plant Research Gatersleben Germany

ISBN: (纸本)3540379517

A novel approach to feature selection from unlabeled vector data is presented. It is based on the reconstruction of original data relationships in an auxiliary space with either weighted or omitted features. Feature weighting, on one hand;is related to the return forces of factors in a parametric data similarity measure as response to disturbance of their optimum values. Feature omission, on the other hand, inducing measurable loss of reconstruction quality, is realized in an iterative greedy way. The proposed framework allows to apply custom data similarity measures. Here, adaptive Euclidean distance and adaptive Pearson correlation are considered, the former serving as standard reference, the latter being, usefully for intensity data. Results of the different strategies are given for chromatography and gene expression data.

关键词： feature selection adaptive similarity measures

来源：评论

学校读者我要写书评

暂无评论

Towards Effective Classification of Imbalanced Data with Convolutional neural networks 7th

Towards Effective Classification of Imbalanced Data with Con...

引用

7th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Raj, Vidwath Magg, Sven Wermter, Stefan Univ Hamburg Dept Informat Knowledge Technol Hamburg Germany

ISBN: (纸本)9783319461823;9783319461816

Class imbalance in machine learning is a problem often found with real-world data, where data from one class clearly dominates the dataset. Most neural network classifiers fail to learn to classify such datasets correctly if class-to-class separability is poor due to a strong bias towards the majority class. In this paper we present an algorithmic solution, integrating different methods into a novel approach using a class-to-class separability score, to increase performance on poorly separable, imbalanced datasets using Cost Sensitive neural networks. We compare different cost functions and methods that can be used for training Convolutional neural networks on a highly imbalanced dataset of multi-channel time series data. Results show that, despite being imbalanced and poorly separable, performance metrics such as G-Mean as high as 92.8% could be reached by using cost sensitive Convolutional neural networks to detect patterns and correctly classify time series from 3 different datasets.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

Fusers Based on Classifier Response and Discriminant Function - Comparative Study

Fusers Based on Classifier Response and Discriminant Functio...

引用

3rd International workshop on Hybrid artificial Intelligence Systems

作者： Wozniak, Michal Jackowski, Konrad Wroclaw Univ Technol Chair Syst & Comp Networks PL-50370 Wroclaw Poland

ISBN: (纸本)9783540876557

The Multiple Classifier Systems are nowadays one of the most promising directions in pattern recognition. There are many methods of decision making by the ensemble of classifiers. The most popular are methods that have their origin in voting method, where the decision of the common classifier is a combination of individual classifiers' decisions. This work presents methods of classifier combination, where neural networks plays a role of fuser block. Fusion on level of recognizer responses or values of their discriminant functions is applied. The qualities of proposed methods are evaluated via computer experiments on generated data and two benchmark databases.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Polyphonic monotimbral music transcription using dynamic networks

Polyphonic monotimbral music transcription using dynamic net...

引用

1st iapr TC3 workshop on artificial neural networks in pattern recognition

作者： Pertusa, A Inesta, JM Univ Alicante Dept Lenguajes & Sistemas Informat E-03080 Alicante Spain

The automatic extraction of the notes that were played in a digital musical signal (automatic music transcription) is an open problem. A number of techniques have been applied to solve it without concluding results. The monotimbral polyphonic version of the problem is posed here: a single instrument has been played and more than one note can sound at the same time. This work tries to approach it through the identification of the pattern of a given instrument in the frequency domain. This is achieved using time-delay neural networks that are fed with the band-grouped spectrogram of a polyphonic monotimbral music recording. The use of a learning scheme based on examples like neural networks permits our system to avoid the use of an auditory model to approach this problem. A number of issues have to be faced to have a robust and powerful system, but promising results using synthesized instruments are presented. (c) 2005 Elsevier B.V. All rights reserved.

关键词： Computer music

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28页 << < 5 6 7 8 9 10 11 12 13 14 > >>

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：