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141-150 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：

Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech 10th

Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instan...

引用

10th iapr TC3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： Matousek, Jindrich Tihelka, Daniel Univ West Bohemia Fac Appl Sci Dept Cybernet Plzen Czech Republic Univ West Bohemia New Technol Informat Soc NTIS Fac Sci Appl Plzen Czech Republic

ISBN: (纸本)9783031206498;9783031206504

In this paper, we propose to frame glottal closure instant (GCI) detection from raw speech as a sequence-to-sequence prediction problem and to explore the potential of recurrent neural networks (RNNs) to handle this problem. We compare the RNN architecture to widely used convolutional neural networks (CNNs) and to some other machine learning-based and traditional non-learning algorithms on several publicly available databases. We show that the RNN architecture improves GCI detection. The best results were achieved for a joint CNN-BiLSTM model in which RNN is composed of bidirectional long short-term memory (BiLSTM) units and CNN layers are used to extract relevant features.

关键词： Glottal closure instant detection Deep learning Recurrent neural network Convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Objectness Scoring and Detection Proposals in Forward-Looking Sonar Images with Convolutional neural networks 7th

Objectness Scoring and Detection Proposals in Forward-Lookin...

引用

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

作者： Valdenegro-Toro, Matias Heriot Watt Univ Sch Engn & Phys Sci Ocean Syst Lab Edinburgh EH14 4AS Midlothian Scotland

ISBN: (纸本)9783319461823;9783319461816

Forward-looking sonar can capture high resolution images of underwater scenes, but their interpretation is complex. Generic object detection in such images has not been solved, specially in cases of small and unknown objects. In comparison, detection proposal algorithms have produced top performing object detectors in real-world color images. In this work we develop a Convolutional neural Network that can reliably score objectness of image windows in forward-looking sonar images and by thresholding objectness, we generate detection proposals. In our dataset of marine garbage objects, we obtain 94% recall, generating around 60 proposals per image. The biggest strength of our method is that it can generalize to previously unseen objects. We show this by detecting chain links, walls and a wrench without previous training in such objects. We strongly believe our method can be used for class-independent object detection, with many real-world applications such as chain following and mine detection.

关键词： Object detection Detection proposals Sonar image processing Forward-looking sonar

来源：评论

学校读者我要写书评

暂无评论

A Study on the Autonomous Detection of Impact Craters 10th

A Study on the Autonomous Detection of Impact Craters

引用

10th iapr TC3 workshop on artificial neural networks for pattern recognition (ANNPR)

作者： Aburaed, Nour Alsaad, Mina Al Mansoori, Saeed Al-Ahmad, Hussain Univ Dubai Dubai U Arab Emirates Mohammed Bin Rashid Space Ctr Dubai U Arab Emirates

ISBN: (纸本)9783031206498;9783031206504

Planet surface studies is one of the most popular research areas in planetary science, as it is useful to attain information about a planet's history and geology without directly landing on its surface. Autonomous detection of craters has been of particular interest lately, especially for Mars and Lunar surfaces. This review study deals with the technical implementation, training, and testing of YOLOv5 and YOLOv6 to gauge their efficiency in detecting craters. YOLOv6 is the most recent member of the YOLO family, and it is believed that it outperform all of its predecessors. In addition to comparing the aforementioned two models, the performance of the most widely used optimization functions, including SGD, Adam, and AdamW is studied as well. The methods are evaluated using mAP and mAR to verify whether YOLOv6 potentially outperforms YOLOv5, and whether AdamW is capable to generalize better than its peer optimizers.

关键词： Object detection Convolutional neural networks YOLO Craters Optimizers

来源：评论

学校读者我要写书评

暂无评论

An empirical comparison of feature reduction methods in the context of microarray data classification

引用

2nd iapr workshop on artificial neural networks in pattern recognition

作者： Kestler, Hans A. Muessel, Christoph Univ Ulm Dept Neural Informat Proc D-89069 Ulm Germany Univ Hosp Ulm Dept Internal Med 1 D-89081 Ulm Germany

ISBN: (纸本)3540379517

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. Measuring the expression of a large number of genes with DNA microarrays may serve this purpose. While the expression level of several thousands of genes can be measured in a single experiment, only a few dozens of experiments are normally carried out, leading to data sets of very high dimensionality and low cardinality. In this situation, feature reduction techniques capable of reducing the dimensionality of data are essential for building predictive tools based on classification. Methods and Data: We compare the popular feature selection and classification method PAM (Tibshirani et al.) to several other methods. Feature reduction and feature ranking methods, such as Random Projection, Random Feature Selection, Area under the ROC curve and PCA are applied. We employ these together with the classification component of PAM, Linear Discriminant Analysis (LDA), a Nearest Prototype (NP) classifier and linear support vector machines (SVMs). We apply these methods to three publicly available linearly separable gene expression data sets of varying cardinality and dimensionality. Results and Conclusions: In our experiments with the gene expression data we could not discover a clearly superior algorithm, instead most surprisingly we found that feature reduction using random projections or selections performed often equally well.

关键词： Oncology

来源：评论

学校读者我要写书评

暂无评论

Grayscale images and RGB video: Compression by morphological neural network

Grayscale images and RGB video: Compression by morphological...

引用

5th INNS iapr TC 3 GIRPR workshop on artificial neural networks for pattern recognition, ANNPR 2012

作者： De Souza, Osvaldo Cortez, Paulo César Da Silva, Francisco A. T. F. Federal University of Ceará DETI Fortaleza Brazil National Institute for Space Research ROEN Eusébio Brazil

ISBN: (纸本)9783642332111

This paper investigates image and RGB video compression by a supervised morphological neural network. This network was originally designed to compress grayscale image and was then extended to RGB video. It supports two kinds of thresholds: a pixel-component threshold and pixel-error counting threshold. The activation function is based on an adaptive morphological neuron, which produces suitable compression rates even when working with three color channels simultaneously. Both intra-frame and inter-frame compression approaches are implemented. The PSNR level indicates that the compressed video is compliant with the desired quality levels. Our results are compared to those obtained with commonly used image and video compression methods. Network application results are presented for grayscale images and RGB video with a 352 x 288 pixel size. © 2012 Springer-Verlag.

关键词： Image compression

来源：评论

学校读者我要写书评

暂无评论

F-Measure Curves for Visualizing Classifier Performance with Imbalanced Data 8th

F-Measure Curves for Visualizing Classifier Performance with...

引用

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

作者： Soleymani, Roghayeh Granger, Eric Fumera, Giorgio Univ Quebec Ecole Technol Super Lab Imagerie Vis & Intelligence Artificielle Montreal PQ Canada Univ Cagliari Dept Elect & Elect Engn Pattern Recognit & Applicat Lab Cagliari Italy

ISBN: (纸本)9783319999784;9783319999777

Training classifiers using imbalanced data is a challenging problem in many real-world recognition applications due in part to the bias in performance that occur for: (1) classifiers that are often optimized and compared using unsuitable performance measurements for imbalance problems;(2) classifiers that are trained and tested on a fixed imbalance level of data, which may differ from operational scenarios;(3) cases where the preference of correct classification of classes is application dependent. Specialized performance evaluation metrics and tools are needed for problems that involve class imbalance, including scalar metrics that assume a given operating condition (skew level and relative preference of classes), and global evaluation curves or metrics that consider a range of operating conditions. We propose a global evaluation space for the scalar F-measure metric that is analogous to the cost curves for expected cost. In this space, a classifier is represented as a curve that shows its performance over all of its decision thresholds and a range of imbalance levels for the desired preference of true positive rate to precision. Experiments with synthetic data show the benefits of evaluating and comparing classifiers under different operating conditions in the proposed F-measure space over ROC, precision-recall, and cost spaces.

关键词： Class imbalance Performance visualization tools F-measure

来源：评论

学校读者我要写书评

暂无评论

On-line handwritten character recognition by a hybrid method based on neural networks and pattern matching 3rd

引用

3rd International workshop on artificial neural networks, IWANN 1995

作者： Cho, Jung-Wook Lee, Soo-Young Park, Cheol Hoon Computation and Neural Systems Laboratory Department of Electrical Engineering Korea Advanced Institute of Science and Technology 373-1 Kusung-Dong Yusung-Gu Taejon305-701 Korea Republic of

ISBN: (纸本)3540594973

A hybrid system is developed for on-line recognition of hand-written Korean characters. Each syllable consists of several Korean alphabets and is written in a rectangular box to result in a 2-dimensional compositions of alphabets. Although only 24 alphabets exist, the number of syllables easily exceeds 3000. Therefore, instead of the syllables, the 24 alphabets are used as basic recognition unit, which causes difficult segmentation and recognition problems. Each Korean alphabet has at most 4 strokes, and 4 neural networks are trained separately for alphabets with different number of strokes. To improve the recognition preformance, after neural network classifications, classical pattern matching is also applied to check validity of the decisions made by the neural network classifiers. The hybrid systems is robust on distortion and rotation of the characters. © 1995, Springer Verlag. All rights reserved.

关键词： Hybrid systems

来源：评论

学校读者我要写书评

暂无评论

Generative Plant Growth Simulation from Sequence-Informed Environmental Conditions 11th

Generative Plant Growth Simulation from Sequence-Informed En...

引用

11th iapr TC3 workshop on artificial neural networks in pattern recognition (ANNPR)

作者： Debbagh, Mohamed Liu, Yixue Zheng, Zhouzhou Jiang, Xintong Sun, Shangpeng Lefsrud, Mark McGill Univ Montreal PQ H3A 0G4 Canada Northwest A&F Univ Coll Mech & Elect Engn Yangling 712100 Shaanxi Peoples R China

ISBN: (纸本)9783031716010;9783031716027

A plant growth simulation can be characterized as a reconstructed visual representation of a plant or plant system. The phenotypic characteristics and plant structures are controlled by the scene environment and other contextual attributes. Considering the temporal dependencies and compounding effects of various factors on growth trajectories, we formulate a probabilistic approach to the simulation task by solving a frame synthesis and pattern recognition problem. We introduce a sequence-informed plant growth simulation framework (SI-PGS) that employs a conditional generative model to implicitly learn a distribution of possible plant representations within a dynamic scene from a fusion of low-dimensional temporal sensor and context data. Methods such as controlled latent sampling and recurrent output connections are used to improve coherence in the plant structures between frames of prediction. In this work, we demonstrate that SI-PGS is able to capture temporal dependencies and continuously generate realistic frames of plant growth.

关键词： Plant Growth Simulation Continuous Frame Synthesis Conditional Generative Model

来源：评论

学校读者我要写书评

暂无评论

3rd International workshop on Reproducible Research in pattern recognition, RRPR 2021

3rd International Workshop on Reproducible Research in Patte...

引用

3rd International workshop on Reproducible Research in pattern recognition, RRPR 2021

ISBN: (纸本)9783030764227

The proceedings contain 13 papers. The special focus in this conference is on Reproducible Research in pattern recognition. The topics include: Reproducibility Aspects of Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors;reproducing the Sparse Huffman Address Map Compression for Deep neural networks;implementation of Genetic Pseudo Rehearsal;reproducibility: Evaluating the Evaluations;torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation;spatio-Temporal Convolutional Autoencoders for Perimeter Intrusion Detection;creating Emotion recognition Algorithms Based on a Convolutional neural Network for Sentiment Analysis;Tree Defect Segmentation Using Geometric Features and CNN;pith Estimation on Tree Log End Images;structure, Concept and Result Reproducibility of the Benchmark on Vesselness Filters;a Heuristic-Based Decision Tree for Connected Components Labeling of 3D Volumes: Implementation and Reproducibility Notes.

关键词：

来源：评论

学校读者我要写书评

暂无评论

pattern Classification Using a Penalized Likelihood Method

Pattern Classification Using a Penalized Likelihood Method

引用

4th workshop on artificial neural networks in pattern recognition

作者： Al-Ani, Ahmed Atiya, Amir F. Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Cairo Univ Dept Comp Engn Giza Egypt

ISBN: (纸本)9783642121586

Penalized likelihood is a well-known theoretically justified approach that has recently attracted attention by the machine learning society. The objective function of the Penalized likelihood consists of the log likelihood of the data minus some term penalizing non-smooth solutions. Subsequently, maximizing this objective function would lead to some sort of trade-off between the faithfulness and the smoothness of the fit. There has been a lot of research to utilize penalized likelihood in regression, however, it is still to be thoroughly investigated in the pattern classification domain. We propose to use a penalty term based on the K-nearest neighbors and an iterative approach to estimate the posterior probabilities. In addition, instead of fixing the value of K for all pattern, we developed a variable K approach, where the number of neighbors can vary from one sample to another. The chosen value of K for a given testing sample is influenced by the K values of its surrounding training samples as well as the most successful K value of all training samples. Comparison with a number of well-known classification methods proved the potential of the proposed method.

关键词： Iterative methods

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 << < 11 12 13 14 15 16 17 18 19 20 > >>

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：