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

550 篇 会议
28 篇 期刊文献
7 册 图书

馆藏范围

585 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

470 篇 工学
- 425 篇 计算机科学与技术...
- 199 篇 软件工程
- 110 篇 电气工程
- 67 篇 信息与通信工程
- 45 篇 生物工程
- 42 篇 生物医学工程（可授...
- 26 篇 控制科学与工程
- 20 篇 光学工程
- 13 篇 仪器科学与技术
- 11 篇 机械工程
- 10 篇 化学工程与技术
- 9 篇 交通运输工程
- 8 篇 电子科学与技术（可...
- 7 篇 土木工程
140 篇 理学
- 69 篇 数学
- 56 篇 生物学
- 33 篇 物理学
- 24 篇 统计学（可授理学、...
- 19 篇 系统科学
- 8 篇 化学
90 篇 管理学
- 63 篇 图书情报与档案管...
- 35 篇 管理科学与工程(可...
- 19 篇 工商管理
46 篇 医学
- 36 篇 临床医学
- 22 篇 基础医学(可授医学...
- 13 篇 药学(可授医学、理...
- 7 篇 特种医学
- 6 篇 公共卫生与预防医...
11 篇 法学
- 9 篇 社会学
11 篇 教育学
- 10 篇 教育学
8 篇 经济学
6 篇 农学

主题

74 篇 machine learning
55 篇 data mining
36 篇 pattern recognit...
33 篇 deep learning
20 篇 feature extracti...
18 篇 artificial intel...
16 篇 support vector m...
15 篇 learning systems
11 篇 image classifica...
9 篇 training
8 篇 neural networks
8 篇 face recognition
8 篇 manifold learnin...
8 篇 supervised learn...
7 篇 pattern classifi...
7 篇 big data
7 篇 clustering algor...
7 篇 classification (...
7 篇 machine learning...
7 篇 data models

机构

4 篇 ukrainian cathol...
3 篇 harbin inst tech...
3 篇 guilin univ elec...
3 篇 fujian univ tech...
3 篇 college of compu...
3 篇 kunming univ col...
3 篇 nanjing univ sci...
3 篇 concordia univ d...
2 篇 shanghai jiao to...
2 篇 australian e-hea...
2 篇 telangana hydera...
2 篇 key lab data gov...
2 篇 idiap ch-1920 ma...
2 篇 department of ma...
2 篇 univ toronto dep...
2 篇 univ montreal di...
2 篇 chitkara univers...
2 篇 natl univ singap...
2 篇 pattern analysis...
2 篇 univ katolik ind...

作者

3 篇 li junbao
3 篇 maier andreas
3 篇 bengio s
3 篇 deepa r.
3 篇 deng fei
3 篇 pan jeng-shyang
3 篇 hu cong
3 篇 zhang daoqiang
3 篇 amelio alessia
3 篇 maier-hein klaus
3 篇 he chunhui
2 篇 vrahatis aristid...
2 篇 jin zhong
2 篇 collobert r
2 篇 tax dmj
2 篇 sharma r
2 篇 cilia nicole dal...
2 篇 fontanella franc...
2 篇 genov r
2 篇 stiefelhagen rai...

语言

568 篇 英文
12 篇 中文
9 篇 其他
1 篇 俄文

检索条件"任意字段=1st International Workshop on Machine Learning and Data Mining in Pattern Recognition"

共 585 条记录，以下是411-420 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：

Multi-task co-clustering via nonnegative matrix factorization

Multi-task co-clustering via nonnegative matrix factorizatio...

引用

international Conference on pattern recognition

作者： Saining Xie Hongtao Lu Yangcheng He Shanghai Jiaotong University China

ISBN: (纸本)9781467322164

Recent results have empirically proved that, given several related tasks with different data distributions and an algorithm that can utilize both the task-specific and cross-task knowledge, clustering performance of each task can be significantly enhanced. This kind of unsupervised learning method is called multi-task clustering. We focus on tackling the multi-task clustering problem via a 3-factor nonnegative matrix factorization. The object of our approach consists of two parts: (1) Within-task co-clustering: co-cluster the data in the input space individually. (2) Cross-task regularization: Learn and refine the relations of feature spaces among different tasks. We show that our approach has a sound information theoretic background and the experimental evaluation shows that it outperforms many state-of-the-art single-task or multi-task clustering methods.

关键词： Clustering algorithms Joints data mining Mutual information Probability distribution Educational institutions machine learning

来源：评论

学校读者我要写书评

暂无评论

Classifying judging states from fMRI data of visual recognition task

Classifying judging states from fMRI data of visual recognit...

引用

2011 1st international workshop on Complexity and data mining, IWCDM 2011

作者： Ni, Huangjing Wang, Wei Pang, Chaoyi Yu, Jiayuan Department of Education Technology Educational Science College Nanjing Normal University Nanjing Jiangsu China Australian E-Health Research Centre CSIRO Australia Department of Psychology Educational Science College Nanjing Normal University Nanjing Jiangsu China

ISBN: (纸本)9781457720079

Identifying the subject's simple judging states from fMRI data is the basis of studying complex logical relationship and has great theoretical significance. In this paper, we study judging states from fMRI data in terms of logical recognition classifications. We found that the ROI (Regions of Interest) regions played an important role in visual recognition task and identified what ROI regions were crucial for the correct judgments. We indicated that the correct and wrong judging states could be identified. In general, our work can answer the questions: (1) what are the judging states when one is performing a visual recognition task;and (2) whether the differences of brain activity pattern exist between the situations of correct and wrong judgments. © 2011 IEEE.

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

data quality assurance and performance measurement of data mining for preventive maintenance of power grid

Data quality assurance and performance measurement of data m...

引用

1st international workshop on data mining for Service and Maintenance, KDD4Service 2011 - Held in Conjunction with SIGKDD'11

作者： Wu, Leon Kaiser, Gail Rudin, Cynthia Anderson, Roger Department of Computer Science Columbia University New York NY 10027 United States MIT Sloan School of Management MIT Cambridge MA 02139 United States Center for Computational Learning Systems Columbia University New York NY 10115 United States

ISBN: (纸本)9781450308427

Ensuring reliability as the electrical grid morphs into the "smart grid" will require innovations in how we assess the state of the grid, for the purpose of proactive maintenance, rather than reactive maintenance;in the future, we will not only react to failures, but also try to anticipate and avoid them using predictive modeling (machine learning and data mining) techniques. To help in meeting this challenge, we present the Neutral Online Visualization-aided Autonomic evaluation framework (NOVA) for evaluating machine learning and data mining algorithms for preventive maintenance on the electrical grid. NOVA has three stages provided through a unified user interface: evaluation of input data quality, evaluation of machine learning and data mining results, and evaluation of the reliability improvement of the power grid. A prototype version of NOVA has been deployed for the power grid in New York City, and it is able to evaluate machine learning and data mining systems effectively and efficiently. Copyright 2011 ACM.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

On the Usefulness of Similarity Based Projection Spaces for Transfer learning

引用

1st international workshop on Similarity-Based pattern recognition (SIMBAD)

作者： Morvant, Emilie Habrard, Amaury Ayache, stephane Aix Marseille Univ Lab Informat Fondamentale Marseille CNRS UMR 6166 F-13453 Marseille 13 France

ISBN: (纸本)9783642244704;9783642244711

Similarity functions are widely used in many machine learning or pattern recognition tasks. We consider here a recent framework for binary classification, proposed by Balcan et al., allowing to learn in a potentially non geometrical space based on good similarity functions. This framework is a generalization of the notion of kernels used in support vector machines in the sense that allows one to use similarity functions that do not need to be positive semi-definite nor symmetric. The similarities are then used to define an explicit projection space where a linear classifier with good generalization properties can be learned. In this paper, we propose to study experimentally the usefulness of similarity based projection spaces for transfer learning issues. More precisely, we consider the problem of domain adaptation where the distributions generating learning data and test data are somewhat different. We stand in the case where no information on the test labels is available. We show that a simple renormalization of a good similarity function taking into account the test data allows us to learn classifiers more performing on the target distribution for difficult adaptation problems. Moreover, this normalization always helps to improve the model when we try to regularize the similarity based projection space in order to move closer the two distributions. We provide experiments on a toy problem and on a real image annotation task.

关键词： Good Similarity Functions Transfer learning Domain Adaptation Image Classification

来源：评论

学校读者我要写书评

暂无评论

Combining data Sources Nonlinearly for Cell Nucleus Classification of Renal Cell Carcinoma

引用

1st international workshop on Similarity-Based pattern recognition (SIMBAD)

作者： Gonen, Mehmet Ulas, Aydin Schueffler, Peter Castellani, Umberto Murino, Vittorio Aalto Univ Sch Sci Dept Informat & Comp Sci HIIT Espoo Finland Univ Verona Dept Comp Sci Verona Italy ETH Dept Comp Sci Zurich Switzerland IIT Genoa Italy

ISBN: (纸本)9783642244704;9783642244711

In kernel-based machine learning algorithms, we can learn a combination of different kernel functions in order to obtain a similarity measure that better matches the underlying problem instead of using a single fixed kernel function. This approach is called multiple kernel learning (MKL). In this paper, we formulate a nonlinear MKL variant and apply it for nuclei classification in tissue microarray images of renal cell carcinoma (RCC). The proposed variant is tested on several feature representations extracted from the automatically segmented nuclei. We compare our results with single-kernel support vector machines trained on each feature representation separately and three linear MKL algorithms from the literature. We demonstrate that our variant obtains more accurate classifiers than competing algorithms for RCC detection by combining information from different feature representations nonlinearly.

关键词： multiple kernel learning renal cell carcinoma support vector machines

来源：评论

学校读者我要写书评

暂无评论

Multiple-Instance learning with Instance Selection via Dominant Sets

引用

1st international workshop on Similarity-Based pattern recognition (SIMBAD)

作者： Erdem, Aykut Erdem, Erkut Hacettepe Univ TR-06800 Ankara Turkey

ISBN: (纸本)9783642244704

Multiple-instance learning (MIL) deals with learning under ambiguity, in which patterns to be classified are described by bags of instances. There has been a growing interest in the design and use of MIL algorithms as it provides a natural framework to solve a wide variety of pattern recognition problems. In this paper, we address MIL from a view that transforms the problem into a standard supervised learning problem via instance selection. The novelty of the proposed approach comes from its selection strategy to identify the most representative examples in the positive and negative training bags, which is based on an effective pairwise clustering algorithm referred to as dominant sets. Experimental results on both standard benchmark data sets and on multi-class image classification problems show that the proposed approach is not only highly competitive with state-of-the-art MIL algorithms but also very robust to outliers and noise.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Model-Based Clustering of Inhomogeneous Paired Comparison data

引用

1st international workshop on Similarity-Based pattern recognition (SIMBAD)

作者： Busse, Ludwig M. Buhmann, Joachim M. ETH Dept Comp Sci CH-8092 Zurich Switzerland

ISBN: (纸本)9783642244704;9783642244711

This paper demonstrates the derivation of a clustering model for paired comparison data. Similarities for non-Euclidean, ordinal data are handled in the model such that it is capable of performing an integrated analysis on real-world data with different patterns of missings. Rank-based pairwise comparison matrices with missing entries can be described and compared by means of a probabilistic mixture model defined on the symmetric group. Our EM-method offers two advantages compared to models for pairwise comparison rank data available in the literature: (i) it identifies groups in the pairwise choices based on similarity (ii) it provides the ability to analyze a data set of heterogeneous character w.r.t. to the structural properties of individal data samples. Furthermore, we devise an active learning strategy for selecting paired comparisons that are highly informative to extract the underlying ranking of the objects. The model can be employed to predict pairwise choice probabilities for individuals and, therefore, it can be used for preference modeling.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

引用

1st international workshop on Similarity-Based pattern recognition, SIMBAD 2011

ISBN: (纸本)9783642244704

The proceedings contain 23 papers. The topics discussed include: on the usefulness of similarity based projection spaces for transfer learning;metric anomaly detection via asymmetric risk minimization;one shot similarity metric learning for action recognition;on a non-monotonicity effect of similarity measures;section-wise similarities for clustering and outlier detection of subjective sequential data;hybrid generative-discriminative nucleus classification of renal cell carcinoma;multi-task regularization of generative similarity models;a generative dyadic aspect model for evidence accumulation clustering;an information theoretic approach to learning generative graph prototypes;impact of the initialization in tree-based fast similarity search techniques;and multiple-instance learning with instance selection via dominant sets.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Novel Supervised Feature Extraction Algorithm Based on Iterative Calculations

Novel Supervised Feature Extraction Algorithm Based on Itera...

引用

12th IEEE international Conference on Information Reuse and Integration (IRI)/1st international workshop on Issues and Challenges in Social Computing (WICSOC)

作者： Takeuchi, Yohei Ito, Momoyo Kashihara, Koji Fukumi, Minoru Univ Tokushima Grad Sch Adv Technol & Sci Tokushima 770 Japan

ISBN: (纸本)9781457709661

In pattern recognition, the principal component analysis (PCA) is one of the most famous feature extraction methods for dimensionality reduction of high-dimensional datasets. Furthermore, Simple-PCA (SPCA) which is a faster version of the PCA, has been carried out effectively by iterative operated learning. However, in SPCA, when input data are distributed in a complex way, SPCA might not be efficient because it is learned without class information of the dataset. Thus, SPCA cannot be said that it is optimal for classification. In this paper, we propose a new learning algorithm, which is learned with the class information of the dataset. Eigenvectors spanning eigenspace of the dataset are obtained by calculation of data variations belonging to each class. We will show the derivation of the proposed algorithm and demonstrate some experiments to compare the SPCA with the proposed algorithm by using UCI datasets.

关键词： pattern recognition principal component analysis supervised learning

来源：评论

学校读者我要写书评

暂无评论

pattern recognition using statistical and neural techniques

Pattern recognition using statistical and neural techniques

引用

1st international Conference on Computer Networks and Information Technology, ICCNIT'11

作者： Ahmad, Tasweer Jameel, Ahlam Ahmad, Balal Government College University Lahore Pakistan

ISBN: (纸本)9781612849416

pattern recognition has become an attractive research oriented field of the computer vision and machine learning for the last few decades. Neural pattern recognition techniques are also being exercised for pattern recognition, showing promising results. In this paper, a comparison is made between statistical and neural pattern recognition techniques and tried to realize how neural techniques reveal far better results than statistical techniques. In this comparison, Discriminant Analysis (DA) and Principal Component Analysis (PCA) are used for pattern recognition, which are a statistical technique. Discriminant Analysis engrosses the problem of huge data dimensions and small sample size. To evade these problems, pattern recognition task is also implemented using Generalized Regression Neural Network (GRNN) and Back-propagation Neural Network (BPNN) techniques. The task of pattern recognition is conceded on a data base of face images of 400 people. Neural networks proved results for better than statistical methods. © 2011 IEEE.

关键词： Discriminant analysis

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59页 << < 38 39 40 41 42 43 44 45 46 47 > >>

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：