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

128 篇 会议
4 册 图书
3 篇 期刊文献

馆藏范围

135 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

115 篇 工学
- 104 篇 计算机科学与技术...
- 29 篇 软件工程
- 13 篇 电气工程
- 11 篇 信息与通信工程
- 9 篇 生物医学工程（可授...
- 9 篇 生物工程
- 6 篇 光学工程
- 4 篇 控制科学与工程
- 3 篇 交通运输工程
- 2 篇 仪器科学与技术
- 1 篇 材料科学与工程（可...
- 1 篇 动力工程及工程热...
- 1 篇 建筑学
- 1 篇 化学工程与技术
- 1 篇 石油与天然气工程
35 篇 理学
- 16 篇 数学
- 16 篇 生物学
- 8 篇 物理学
- 7 篇 统计学（可授理学、...
- 1 篇 化学
- 1 篇 系统科学
14 篇 医学
- 13 篇 临床医学
- 5 篇 基础医学(可授医学...
- 4 篇 药学(可授医学、理...
- 1 篇 公共卫生与预防医...
- 1 篇 特种医学
10 篇 管理学
- 6 篇 图书情报与档案管...
- 4 篇 管理科学与工程(可...
- 2 篇 工商管理
2 篇 农学
- 2 篇 作物学

主题

21 篇 neural networks
7 篇 pattern recognit...
7 篇 artificial intel...
7 篇 artificial neura...
5 篇 neurons
4 篇 deep learning
4 篇 deep neural netw...
4 篇 artificial neura...
4 篇 training
3 篇 face recognition
3 篇 feature extracti...
3 篇 recurrent neural...
2 篇 mixture modeling
2 篇 training data
2 篇 image segmentati...
2 篇 data mining and ...
2 篇 convolution
2 篇 semisupervised l...
2 篇 computed tomogra...
2 篇 independent comp...

机构

8 篇 univ ulm inst ne...
5 篇 univ ulm dept ne...
2 篇 tech univ claust...
2 篇 univ jaume 1 dep...
2 篇 univ hosp ulm de...
2 篇 univ siena dipar...
2 篇 univ siena diism...
2 篇 univ sherbrooke ...
2 篇 univ leipzig ins...
1 篇 medical imaging ...
1 篇 dipartimento di ...
1 篇 national institu...
1 篇 department of bi...
1 篇 nanyang technol ...
1 篇 univ sao paulo i...
1 篇 university of si...
1 篇 tech univ claust...
1 篇 gene expression ...
1 篇 natl tsing hua u...
1 篇 ipk gatersleben ...

作者

13 篇 schwenker friedh...
7 篇 palm guenther
5 篇 kestler hans a.
4 篇 hernandez-espino...
4 篇 fernandez-redond...
4 篇 torres-sospedra ...
3 篇 trentin edmondo
3 篇 hammer barbara
3 篇 bianchini monica
2 篇 granger eric
2 篇 sarti lorenzo
2 篇 villmann thomas
2 篇 maggini marco
2 篇 ziou d
2 篇 lin feng
2 篇 windeatt terry
2 篇 friedhelm schwen...
2 篇 fausser stefan
2 篇 piantadosi gabri...
2 篇 hasenfuss alexan...

语言

134 篇 英文
1 篇 中文

检索条件"任意字段=2nd IAPR Workshop on Artificial Neural Networks in Pattern Recognition"

共 135 条记录，以下是11-20 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：

Fuzzy labeled self-organizing map with label-adjusted prototypes

引用

2nd iapr workshop on artificial neural networks in pattern recognition

作者： Villmann, Thomas Seiffert, Udo Schleif, Frank-Michael Bruess, Cornelia Geweniger, Tina Hammer, Barbara Univ Leipzig Dept Med D-7010 Leipzig Germany IPK Gatersleben Pattern Recognit Grp Gatersleben Germany BRUKER Daltonik Leipzig Numer Toolbox Grp Leipzig Germany Univ Leipzig Inst Comp Sci D-7010 Leipzig Germany Tech Univ Clausthal Inst Comp Sci D-3392 Clausthal Zellerfeld Germany

ISBN: (纸本)3540379517

We extend the self-organizing map (SOM) in the form as proposed by Heskes to a supervised fuzzy classification method. On the one hand, this leads to a robust classifier where efficient learning with fuzzy labeled or partially contradictory data is possible. On the other hand, the integration of labeling into the location of prototypes in a SOM leads to a visualization of those parts of the data relevant for the classification.

关键词： pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Supervised batch neural gas

引用

2nd iapr workshop on artificial neural networks in pattern recognition

作者： Hammer, Barbara Hasenfuss, Alexander Schleif, Frank-Michael Villmann, Thomas Tech Univ Clausthal Inst Comp Sci D-3392 Clausthal Zellerfeld Germany Univ Leipzig Inst Comp Sci D-7010 Leipzig Germany Univ Leipzig Clin Psychotherapy D-7010 Leipzig Germany

ISBN: (纸本)3540379517

Recently, two extensions of neural gas have been proposed: a fast batch version of neural gas for data given in advance, and extensions of neural gas to learn a (possibly fuzzy) supervised classification. Here we propose a batch version for supervised neural gas training which allows to efficiently learn a prototype-based classification, provided training data are given beforehand. The method relies on a simpler cost function than online supervised neural gas and leads to simpler update formulas. We prove convergence of the algorithm in a general framework, which also incorporates supervised k-means and supervised batch-SOM, and which opens the way towards metric adaptation as well as application to proximity data not embedded in a real-vector space.

关键词： neural networks

来源：评论

学校读者我要写书评

暂无评论

Object detection and feature base learning with sparse convolutional neural networks

引用

2nd iapr workshop on artificial neural networks in pattern recognition

作者： Gepperth, Alexander R. T. Inst Neural Dynam D-44780 Bochum Germany

ISBN: (纸本)3540379517

A new convolutional neural network model termed sparse convolutional neural network (SCNN) is presented and its usefulness for real-time object detection in gray-valued, monocular video sequences is demonstrated. SCNNs are trained on "raw" gray values and are intended to perform feature selection as a part of regular neural network training. For this purpose, the learning rule is extended by an unsupervised component which performs a local nonlinear principal components analysis: in this way, meaningful and diverse properties can be computed from local image patches. The SCNN model can be used to train classifiers for different object classes which share a common first layer, i.e., a common preprocessing. This is of advantage since the information needs only to be calculated once for all classifiers. It is further demonstrated how SCNNs can be implemented by successive convolutions of the input image: scanning an image for objects at all possible locations is shown to be possible in real-time using this technique.

关键词： neural networks

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

Learning and feature selection using the set covering machine with data-dependent rays on gene expression profiles

引用

2nd iapr workshop on artificial neural networks in pattern recognition

作者： Kestler, Hans A. Lindner, Wolfgang Mueller, Andre Univ Ulm Neural Informat Proc D-89069 Ulm Germany Univ Hosp Ulm D-89081 Ulm Germany

ISBN: (纸本)3540379517

Microarray technologies are increasingly being used in biological and medical sciences for high throughput analyses of genetic information on the genome, transcriptome and proteome levels. The differentiation between cancerous and benign processes in the body often poses a difficult diagnostic problem in the clinical setting while being of major importance for the treatment of patients. In this situation, feature reduction techniques capable of reducing the dimensionality of data are essential for building predictive tools based on classification. We extend the set covering machine of Marchand and Shawe-Taylor to data dependent rays in order to achieve a feature reduction and direct interpretation of the found conjunctions of intervals on individual genes. We give bounds for the generalization error as a function of the amount of data compression and the number of training errors achieved during training. In experiments with artificial data and a real world data set of gene expression profiles from the pancreas we show the utility of the approach and its applicability to microarray data classification.

关键词： Learning systems

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

artificial neural networks in pattern recognition - Second iapr workshop, ANNPR 2006, Proceedings

引用

2nd iapr workshop on artificial neural networks in pattern recognition, ANNPR 2006

ISBN: (纸本)3540379517

The proceedings contain 26 papers. The topics discussed include: simple and effective connectionist nonparametric estimation of probability density functions;comparison between two spatio-temporal organization maps for speech recognition;adaptive feedback inhibition improves pattern discrimination learning;supervised batch neural gas;fuzzy labeled self-organizing map with label-adjusted prototypes;on the effects of constraints in semi-supervised hierarchical clustering;a study of robustness of KNN classifiers trained using soft labels;an experimental study on training radial basis functions by gradient descent;a local tangent space alignment based transductive classification algorithm;incremental manifold learning via tangent space alignment;a convolutional neural network tolerant of synaptic faults for low-power analog hardware;and fast training and linear programming support vector machines using decomposition techniques.

关键词： neural networks

来源：评论

学校读者我要写书评

暂无评论

Modified minimum classification error learning and its application to neural networks 7th

引用

7th Joint iapr International workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd International workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Shimodaira, Hiroshi Rokui, Jun Nakai, Mitsuru School of Information Science Japan Advanced Institute of Science and Technology TatsunokuchiIshikawa923-1292 Japan

ISBN: (纸本)3540648585

A novel method to improve the generalization performance of the Minimum Classification Error (MCE) / Generalized Probabilistic Descent (GPD) learning is proposed. The MCE / GPD learning proposed by Juang and Katagiri in 1992 results in better recognition performance than the maximum-likelihood (ML) based learning in various areas of pattern recognition. Despite its superiority in recognition performance, it still suffers from the problem of "over-fitting" to the training samples as it is with other learning algorithms. In the present study, a regularization technique is employed to the MCE learning to overcome this problem. Feed-forward neural networks are employed as a recognition platform to evaluate the recognition performance of the proposed method. recognition experiments are conducted on several sorts of datasets. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Maximum likelihood

来源：评论

学校读者我要写书评

暂无评论

On virtually binary nature of probabilistic neural networks 7th

引用

7th Joint iapr International workshop on Structural and Syntactic pattern recognition, SSPR 1998 and 2nd International workshop on Statistical Techniques in pattern recognition, SPR 1998

作者： Grim, Jiří Pudil, Pavel Institute of Information Theory and Automation Academy of Sciences of the Czech Republic P.O. Box 18 Prague 8CZ-18208 Czech Republic

ISBN: (纸本)3540648585

A sequential design of multilayer probabilistic neural networks is considered in the framework of statistical decision-making. Parameters and interconnection structure are optimized layer-by-layer by estimating unknown probability distributions on input space in the form of finite distribution mixtures. The components of mixtures correspond to neurons which perform an information preserving transform between consecutive layers. Simultaneously the entropy of the transformed distribution is minimized. It is argued that in multidimensional spaces and particularly at higher levels of multilayer feed forward neural networks, the output variables of probabilistic neurons tend to be binary. It is shown that the information loss caused by the binary approximation of neurons can be suppressed by increasing the approximation accuracy. © Springer-Verlag Berlin Heidelberg 1998.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

Real-time emotion recognition from speech using echo state networks

引用

3rd iapr workshop on artificial neural networks in pattern recognition

作者： Scherer, Stefan Oubbati, Mohamed Schwenker, Friedhelm Palm, Guenther Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany

ISBN: (纸本)9783540699385

The goal of this work is to investigate real-time emotion recognition in noisy environments. Our approach is to solve this problem using novel recurrent neural networks called echo state networks (ESN). ESNs utilizing the sequential characteristics of biologically motivated modulation spectrum features are easy to train and robust towards noisy real world conditions. The standard Berlin Database of Emotional Speech is used to evaluate the performance of the proposed approach. The experiments reveal promising results overcoming known difficulties and drawbacks of common approaches.

关键词： Recurrent 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14页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：