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

3,117 篇 会议
142 册 图书
22 篇 期刊文献

馆藏范围

3,280 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,140 篇 工学
- 1,954 篇 计算机科学与技术...
- 1,096 篇 软件工程
- 404 篇 信息与通信工程
- 285 篇 电气工程
- 267 篇 生物工程
- 217 篇 控制科学与工程
- 172 篇 生物医学工程（可授...
- 79 篇 化学工程与技术
- 73 篇 光学工程
- 72 篇 机械工程
- 67 篇 仪器科学与技术
- 65 篇 电子科学与技术（可...
- 62 篇 安全科学与工程
- 55 篇 动力工程及工程热...
- 54 篇 网络空间安全
776 篇 理学
- 429 篇 数学
- 295 篇 生物学
- 151 篇 统计学（可授理学、...
- 148 篇 物理学
- 127 篇 系统科学
- 89 篇 化学
505 篇 管理学
- 306 篇 图书情报与档案管...
- 231 篇 管理科学与工程(可...
- 95 篇 工商管理
218 篇 医学
- 193 篇 临床医学
- 125 篇 基础医学(可授医学...
- 87 篇 药学(可授医学、理...
- 55 篇 公共卫生与预防医...
72 篇 法学
- 70 篇 社会学
51 篇 教育学
- 47 篇 教育学
35 篇 经济学
31 篇 农学
6 篇 艺术学
4 篇 军事学
1 篇 文学

主题

568 篇 data mining
543 篇 machine learning
242 篇 pattern recognit...
195 篇 deep learning
174 篇 feature extracti...
154 篇 artificial intel...
153 篇 support vector m...
113 篇 accuracy
113 篇 machine learning...
110 篇 training
93 篇 data models
80 篇 neural networks
78 篇 data mining and ...
65 篇 classification
62 篇 sentiment analys...
61 篇 classification a...
59 篇 big data
57 篇 predictive model...
50 篇 face recognition
48 篇 prediction algor...

机构

7 篇 maharishi school...
6 篇 guangdong univer...
5 篇 galgotias univer...
5 篇 centre of interd...
5 篇 pune
5 篇 chitkara univers...
5 篇 king fahd univer...
5 篇 uttaranchal univ...
5 篇 uttaranchal inst...
5 篇 institute of aut...
4 篇 indian institute...
4 篇 shanghai jiao to...
4 篇 chitkara univers...
4 篇 school of inform...
4 篇 indian statistic...
4 篇 south china univ...
4 篇 zhejiang univers...
4 篇 maharishi school...
4 篇 department of cs...
4 篇 university of ca...

作者

9 篇 de-shuang huang
7 篇 coenen frans
7 篇 venkatesh svetha
7 篇 petra perner
7 篇 tianrui li
6 篇 ryszard tadeusie...
6 篇 marcin korytkows...
6 篇 jacek m. zurada
6 篇 krasotkina olga
6 篇 lotfi a. zadeh
6 篇 mottl vadim
6 篇 leszek rutkowski
5 篇 vitoantonio bevi...
5 篇 sankar k. pal
4 篇 waiyamai kitsana
4 篇 sozo inoue
4 篇 sergei o. kuznet...
4 篇 duoqian miao
4 篇 shengli xie
4 篇 hepu deng

语言

2,835 篇 英文
436 篇 其他
45 篇 中文
1 篇 西班牙文
1 篇 土耳其文

检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3281 条记录，以下是2401-2410 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：

learning from imbalanced data using ensemble methods and cluster-based undersampling 3

Learning from imbalanced data using ensemble methods and clu...

引用

3rd international Workshop on New Frontiers in mining Complex patterns, NFMCP 2014 held in Conjunction with European conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML-PKDD 2014

作者： Sobhani, Parinaz Viktor, Herna Matwin, Stan School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada Faculty of Computer Science Dalhousie University Halifax Canada

ISBN: (纸本)9783319178752

Imbalanced data, where the number of instances of one class is much higher than the others, are frequent in many domains such as fraud detection, telecommunications management, oil spill detection, and text classification. Traditional classifiers do not perform well when considering data that are susceptible to both within-class and between-class imbalances. In this paper, we propose the ClusFirstClass algorithm that employs cluster analysis to aid classifiers when aiming to build accurate models against such imbalanced datasets. In order to work with balanced classes, all minority instances are used together with the same number of majority instances. To further reduce the impact of within-class imbalance, majority instances are clustered into different groups and at least one instance is selected from each cluster. Experimental results demonstrate that our proposed ClusFirstClass algorithm yields promising results compared to the state-of-the art classification approaches, when evaluated against a number of highly imbalanced datasets. © Springer international Publishing Switzerland 2015.

关键词： Cluster analysis

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised learning for multi-target regression 3

Semi-supervised learning for multi-target regression

引用

作者： Levatić, Jurica Ceci, Michelangelo Kocev, Dragi Džeroski, Sašo Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia Jožef Stefan International Postgraduate School Ljubljana Slovenia Department of Informatics University of Bari Aldo Moro Bari Italy

ISBN: (纸本)9783319178752

The most common machine learning approach is supervised learning, which uses labeled data for building predictive models. However, in many practical problems, the availability of annotated data is limited due to the expensive, tedious and time-consuming annotation procedure. At the same, unlabeled data can be easily available in large amounts. This is especially pronounced for predictive modelling problems with a structured output space and complex labels. Semi-supervised learning (SSL) aims to use unlabeled data as an additional source of information in order to build better predictive models than can be learned from labeled data alone. The majority of work in SSL considers the simple tasks of classification and regression where the output space consists of a single variable. Much less work has been done on SSL for structured output prediction. In this study, we address the task of multi-target regression (MTR), a type of structured output prediction, where the output space consists of multiple numerical values. Our main objective is to investigate whether we can improve over supervised methods for MTR by using unlabeled data. We use ensembles of predictive clustering trees in a self-training fashion: the most reliable predictions (passing a reliability threshold) on unlabeled data are iteratively used to re-train the model. We use the variance of the ensemble models’ predictions as an indicator of the reliability of predictions. Our results provide a proof-of-concept: The use of unlabeled data improves the predictive performance of ensembles for multi-target regression, but further efforts are needed to automatically select the optimal threshold for the reliability of predictions. © Springer international Publishing Switzerland 2015.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Classification and Optimization of Decision Trees for Inconsistent Decision Tables Represented as MVD tables

Classification and Optimization of Decision Trees for Incons...

引用

3rd international conference on Innovative Network Systems and Applications (iNetSApp) held in conjunction with Federated conference on Computer Science and Information Systems (FedCSIS)

作者： Azad, Mohammad Moshkov, Mikhail King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia

ISBN: (纸本)9788360810668

Decision tree is a widely used technique to discover patterns from consistent data set. But if the data set is inconsistent, where there are groups of examples (objects) with equal values of conditional attributes but different decisions (values of the decision attribute), then to discover the essential patterns or knowledge from the data set is challenging. We consider three approaches (generalized, most common and many-valued decision) to handle such inconsistency. We created different greedy algorithms using various types of impurity and uncertainty measures to construct decision trees. We compared the three approaches based on the decision tree properties of the depth, average depth and number of nodes. Based on the result of the comparison, we choose to work with the many-valued decision approach. Now to determine which greedy algorithms are efficient, we compared them based on the optimization and classification results. It was found that some greedy algorithms (Mult_ws_entSort, and Mult_ws_entML) are good for both optimization and classification.

关键词： decision tables decision trees greedy algorithms optimisation pattern classification MVD tables Mult_ws_entML greedy algorithm Mult_ws_entSort greedy algorithm average depth conditional attributes consistent data set decision attribute values decision tree classification decision tree optimization generalized approach impurity measures inconsistency handling inconsistent data set inconsistent decision tables knowledge discovery many-valued decision approach most-common approach node number pattern discovery uncertainty measures Artificial intelligence Decision trees Greedy algorithms Heuristic algorithms Impurities Measurement uncertainty Uncertainty Decision Trees greedy algorithm Decision tables mean depth Heuristic algorithms dataset measurement uncertainty pattern recognition data mining Artificial Intelligence

来源：评论

学校读者我要写书评

暂无评论

Multiple priority dispatching rules for the job shop scheduling problem

Multiple priority dispatching rules for the job shop schedul...

引用

international conference on Control, Engineering & Information Technology (CEIT)

作者： Mohamed Habib Zahmani Baghdad Atmani Abdelghani Bekrar Nassima Aissani LMPA University of Mostaganem Algeria LIO University of Oran 1 Algeria University of Valenciennes and Hainaut Cambrésis France

In this paper we focus on the Job Shop Scheduling Problem (JSSP) using Priority Dispatching Rules. Simulation model for makespan optimization is proposed using different Dispatching Rules (DR) for each machine in the shop floor. Collected results are used for learning base construction. This database will be used to develop an inference model able to select the best DR for every new scheduling problem. This preliminary study shows advantages of using different DR and also saving progress JSSP data.

关键词： Dispatching Job shop scheduling data mining databases data models Object recognition Optimization

来源：评论

学校读者我要写书评

暂无评论

Workshop I: international workshop on learning Semantics for Multimedia Big data (LSMBD)

Workshop I: International workshop on Learning Semantics for...

引用

Asian conference on pattern recognition (ACPR)

作者： Kai-Lung Hua Wen-Huang Cheng Lai-Kuan Wong Chin-Kuan Ho National Taiwan University of Science and Technology Taiwan Academia Sinica Taiwan Multimedia University Malaysia

Recent advance and development of multimedia technologies have enlightened the creation of intuitive and ubiquitous human-computer communication environment to make our life easier. Such progress of emerging multimedia technologies would result in revolutionary changes of our daily practices, such as shopping, traveling, and social networking. Recently the increasingly massive and complex multimedia data bring new challenges for learning semantics: as data volumes grow to the extent, the traditional solution will not be able to handle effectively. The workshop on learning Semantics for Multimedia Big data, LSMBD 2015, aims to bring together researchers and professionals from worldwide academia and industry for showcasing, discussing, and reviewing the whole spectrum of technological opportunities, challenges, solutions, and emerging applications in learning semantics for big data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Denoising AutoEncoder in Neural Networks with Modified Elliott Activation Function and Sparsity-Favoring Cost Function

Denoising AutoEncoder in Neural Networks with Modified Ellio...

引用

international conference on Applied Computing and Information Technology/international conference on Computational Science and Intelligence (ACIT-CSI)

作者： Hasham Burhani Wenying Feng Gongzhu Hu Departments of Computing & Information Systems and Mathematics Trent University Peterborough Ontario Canada Department of Computer Science Central Michigan University Mount Pleasant Michigan USA

Neural networks (NN) are architectures and algorithms for machine learning. They are quite powerful for tasks like classification, clustering, and pattern recognition. Large neural networks can be considered a universal function that can approximate any function, and hence are effective at learning from the training data, not only the useful information but also the noise in the training data. However, as the number of neurons and the number of hidden layers grow, the number of connections in the network increases exponentially, and the over fitting problem becomes more severe biased towards noise. Various methods have been proposed to address this problem such as AutoEncoder, Dropout, DropConnect, and Factored Mean training. In this paper, we propose a denoising autoencoder approach using a modified Elliott activation function and a cost function that favors sparsity in the input data. Preliminary experiments using the modified algorithm on several real data sets showed that the proposed approach performed well.

关键词： Neurons Biological neural networks Training Noise reduction Cost function machine learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Evaluation of different data-derived label hierarchies in multi-label classification 3

Evaluation of different data-derived label hierarchies in mu...

引用

作者： Madjarov, Gjorgji Dimitrovski, Ivica Gjorgjevikj, Dejan Džeroski, Sašo Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Rudgjer Boshkovikj 16 Skopje1000 Macedonia Jožef Stefan Institute Jamova Cesta 39 Ljubljana1000 Slovenia

ISBN: (纸本)9783319178752

Motivated by an increasing number of new applications, the research community is devoting an increasing amount of attention to the task of multi-label classification (MLC). Many different approaches to solving multi-label classification problems have been recently developed. Recent empirical studies have comprehensively evaluated many of these approaches on many datasets using different evaluation measures. The studies have indicated that the predictive performance and efficiency of the approaches could be improved by using data derived (artificial) hierarchies, in the learning and prediction phases. In this paper, we compare different clustering algorithms for constructing the label hierarchies (in a data-driven manner), in multi-label classification. We consider flat label sets and construct the label hierarchies from the label sets that appear in the annotations of the training data by using four different clustering algorithms (balanced k-means, agglomerative clustering with single and complete linkage and predictive clustering trees). The hierarchies are then used in conjunction with global hierarchical multi-label classification (HMC) approaches. The results from the statistical and experimental evaluation reveal that the data-derived label hierarchies used in conjunction with global HMC methods greatly improve the performance of MLC methods. Additionally, multi-branch hierarchies appear much more suitable for the global HMC approaches as compared to the binary hierarchies. © Springer international Publishing Switzerland 2015.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Distributed forests for MapReduce-based machine learning

Distributed forests for MapReduce-based machine learning

引用

Asian conference on pattern recognition (ACPR)

作者： Ryoji Wakayama Ryuei Murata Akisato Kimura Takayoshi Yamashita Yuji Yamauchi Hironobu Fujiyoshi Chubu Daigaku Kasugai Aichi JP Chubu University Japan NTT Communication Science Laboratories Japan

ISBN: (纸本)9781479961016

This paper proposes a novel method for training random forests with big data on MapReduce clusters. Random forests are well suited for parallel distributed systems, since they are composed of multiple decision trees and every decision tree can be independently trained by ensemble learning methods. However, naive implementation of random forests on distributed systems easily overfits the training data, yielding poor classification performances. This is because each cluster node can have access to only a small fraction of the training data. The proposed method tackles this problem by introducing the following three steps. (1) "Shared forests" are built in advance on the master node and shared with all the cluster nodes. (2) With the help of transfer learning, the shared forests are adapted to the training data placed on each cluster node. (3) The adapted forests on every cluster node are returned to the master node, and irrelevant trees yielding poor classification performances are removed to form the final forests. Experimental results show that our proposed method for MapReduce clusters can quickly learn random forests without any sacrifice of classification performance.

关键词： Training data Training Decision trees Distributed databases Vegetation Computer architecture learning systems

来源：评论

学校读者我要写书评

暂无评论

Deep learning using heterogeneous feature maps for maxout networks

Deep learning using heterogeneous feature maps for maxout ne...

引用

Asian conference on pattern recognition (ACPR)

作者： Yasunori Ishii Reiko Hagawa Sotaro Tsukizawa Panasonic Corporation Kadoma City Osaka Japan

ISBN: (纸本)9781479961016

We propose a novel type of maxout that uses filters with kernels of multiple sizes for generating convolved maps. These filters extract the most effective features for recognition from many different variations of texture patterns. A convolved map is generated by convolution between feature maps and filters. If the size of filters is varied, the size of the convolved map will also vary; in which case, since there are no correspondences among the positions of convolved maps, maxout will not work for these kinds of filters. To align the sizes of convolved maps, we converted, in advance, feature maps, which we term `heterogeneous feature maps,' using zero padding. Converting the size of feature maps in this way allows maxout to function, even with filters of different sizes. In this study we demonstrate the classification performances using our proposed maxout on MNIST, CIFAR-10, CIFAR-100, SVHN datasets, and show a 13.17% improvement of accuracy with augmented data.

关键词： Kernel Feature extraction Convolution Error analysis Neural networks Training machine learning

来源：评论

学校读者我要写书评

暂无评论

Evaluation of the online assessment test using process mining (Case Study: Intensive English Center)

Evaluation of the online assessment test using process minin...

引用

international conference of Information and Communication Technology (ICoICT)

作者： Vinca Aisa Angelina Prima Kurniati A W Yanuar Firdaus School of Computing Telkom University Bandung Indonesia

One process in e-learning is Online Assessment Test which is aimed to determine students' understanding of the materials. Online Assessment Test is usually using Multiple Choice Questions (MCQ). Students are not always answer MCQ sequentially, so that each student can have navigational pattern, which can be different from the predefined navigational model. Different navigational patterns can result in different test performance too. This paper presents the process and result of implementing process mining to compare students answering patterns to the predetermined navigational model of MCQ in online assessment test. Process mining is being used to model the business process flow based on the event log of the information system. In this case, online assessment test is the business process and e-learning system is the information system being analyzed. The output of the process are the test marks, performance, students' strength and weakness, and the analysis of navigational patterns compared to the predefined navigational model.

关键词： Navigation Analytical models data mining IEC data models Electronic learning

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329页 << < 237 238 239 240 241 242 243 244 245 246 > >>

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：