检索结果-内蒙古大学图书馆

IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Zhou, Ruizhi Niu, Lingfeng Qi, Zhiquan Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China

ISBN: (纸本)9781467396189

The image restoration task is often modelled as an unconstrained minimization, which is composed of a quadratic data-fidelity term and a regularization term. Recently, nonsmooth regularization received significant attentions due to its excellent empirical performance on the presence of edges. To handle the nonsmoothness of the cost function, we propose a smoothing trust region algorithm in this paper. In order to make the new method more efficient and more suitable for big data situation, the Steihaug-CG algorithm is utilized to solve the trust region subproblem. New algorithm is compared with the Lucy-Richardson method. Numerical experiments on several images validate the effectiveness of the proposed method.

关键词： Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Linear Twin SVM for Learning from label Proportions

Linear Twin SVM for Learning from Label Proportions

引用

IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Bo Wang Zhensong Chen Zhiquan Qi The Key Research Lab on Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Management University of Chinese Academy of Sciences Beijing China

In this paper, we study the problem of learning from label proportions in which label information of data is provided in bag level. In this kind of problem, training data is grouped into various bags and only the proportions of positive instances is known. Inspired by proportion-SVM, we propose a new classification model based on twin SVM, which is also in a large-margin framework and only needs to solve two smaller problems. Avoiding making restrictive assumptions of the data, our model can learn the labels of every single instance based on group proportions information. In order to solve the non-convex problem in our new model, we propose an alternative algorithm to obtain the optimal solution efficiently. Also, we prove the effectiveness of our method in theoretical and experimental way.

关键词： Support vector machines Training Yttrium Linear programming Diseases Computational modeling Analytical models

来源：评论

学校读者我要写书评

暂无评论

GOMES: A GROUP-AWARE MULTI-VIEW FUSION APPROACH TOWARDS REAL-WORLD IMAGE CLUSTERING

GOMES: A GROUP-AWARE MULTI-VIEW FUSION APPROACH TOWARDS REAL...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xue, Zhe Li, Guorong Wang, Shuhui Zhang, Chunjie Zhang, Weigang Huang, Qingming Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci Inst Comput Tech Key Lab Intell Info Proc Beijing Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Weihai Peoples R China

ISBN: (纸本)9781479970827

Different features describe different views of visual appearance, multi-view based methods can integrate the information contained in each view and improve the image clustering performance. Most of the existing methods assume that the importance of one type of feature is the same to all the data. However, the visual appearance of images are different, so the description abilities of different features vary with different images. To solve this problem, we propose a groupaware multi-view fusion approach. Images are partitioned into groups which consist of several images sharing similar visual appearance. We assign different weights to evaluate the pairwise similarity between different groups. Then the clustering results and the fusion weights are learned by an iterative optimization procedure. Experimental results indicate that our approach achieves promising clustering performance compared with the existing methods.

关键词： multi-view learning group-aware fusion image clustering

来源：评论

学校读者我要写书评

暂无评论

World Search Engine IQ Test Based on the Internet IQ Evaluation Algorithms

引用

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2015年第2期14卷 221-237页

作者： Liu, Feng Shi, Yong Wang, Bo Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

With increasing concern about Internet intelligence, this paper proposes concepts of the Internet and Internet subsystem IQs over search engines. Based on human IQ calculations, the paper first establishes a 2014 Internet Intelligence Scale and designs an intelligence test bank regarding search engines. Then, an intelligence test using such test bank is carried out on 50 typical search engines from 25 countries and regions across the world. Meanwhile, another intelligence test is also conducted in the human age groups of 6, 12 and 18 years using the same test bank and rules. Next, the absolute IQ and the relative IQ of the 53 test subjects are calculated in accordance with the test results. Finally, the data mining method is applied to find out the features of search engines reflected in the Internet intelligence test and the intelligence difference between search engines and human beings.

关键词： Search engine absolute IQ deviation IQ clustering classification

来源：评论

学校读者我要写书评

暂无评论

Prediction of Sequential Static Input-Output Table

Prediction of Sequential Static Input-Output Table

引用

IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Wen Long Huiwen Wang Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China School of Economics and Management Beihang University Beijing China

It is necessary to take much time and resources for compiling an input-output table, and there inevitably exists time lag in all the finished tables, which has greatly weakened the analysis and application function of input-output table. Though the development of techniques of adjusting input coefficient has relieved disadvantage of time lag to some extent, yet this kind of methods is still inapplicable to predictive problem. Consider that internal constraints of the table, this paper proposes a new approach based on the sequential tables series to solve the problem of forecasting the future input-output table.

关键词： Mathematical model Economics Forecasting Predictive models Matrix converters Industries

来源：评论

学校读者我要写书评

暂无评论

引用

IEEE International Conference on Computer Vision

作者： Song, Guoli Wang, Shuhui Huang, Qingming Tian, Qi Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci Key Lab Intell Info Proc Inst Comput Tech Beijing Peoples R China Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

ISBN: (纸本)9781467383905

data from real applications involve multiple modalities representing content with the same semantics and deliver rich information from complementary aspects. However, relations among heterogeneous modalities are simply treated as observation-to-fit by existing work, and the parameterized cross-modal mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy of multi-modal data. In this paper, we build our work based on Gaussian process latent variable model (GPLVM) to learn the non-linear non-parametric mapping functions and transform heterogeneous data into a shared latent space. We propose multi-modal Similarity Gaussian Process latent variable model (m-SimGP), which learns the nonlinear mapping functions between the intra-modal similarities and latent representation. We further propose multi-modal regularized similarity GPLVM (m-RSimGP) by encouraging similar/dissimilar points to be similar/dissimilar in the output space. The overall objective functions are solved by simple and scalable gradient decent techniques. The proposed models are robust to content divergence and high-dimensionality in multi-modal representation. They can be applied to various tasks to discover the non-linear correlations and obtain the comparable low-dimensional representation for heterogeneous modalities. On two widely used real-world datasets, we outperform previous approaches for cross-modal content retrieval and cross-modal classification.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

A New Intrusion Detection Approach using PSO based Multiple Criteria Linear Programming 3

A New Intrusion Detection Approach using PSO based Multiple ...

引用

3rd International Conference on Information Technology and Quantitative management (ITQM)

作者： Bamakan, Seyed Mojtaba Hosseini Amiri, Behnam Mirzabagheri, Mahboubeh Shi, Yong Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100090 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100090 Peoples R China Univ Tehran Dept IT Management Tehran Iran Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Intrusion detection system (IDS) is an inseparable part of each computer networks to monitor the events and attacks, which many researchers proposed variety of models to improve the performance of IDS. In this paper we present a new method based on multiple criteria linear programming and particle swarm optimization to enhance the accuracy of attacks detection. Multiple criteria linear programming is a classification method based on mathematical programming which has been showed a potential ability to solve real-life data mining problems. However, tuning its parameters is an essential steps in training phase. Particle swarm optimization (PSO) is a robust and simple to implement optimization technique has been used in order to improve the performance of MCLP classifier. KDD CUP 99 dataset used to evaluate the performance of proposed method. The result demonstrated the proposed model has comparable performance based on detection rate, false alarm rate and running time compare to two other benchmark classifiers. (C) 2015 Published by Elsevier B.V.

关键词： Network Intrusion detection Multiple criteria linear programming Particle swarm optimization Classification data mining

来源：评论

学校读者我要写书评

暂无评论

Pavement Distress Detection Using Random Decision Forests 1

引用

2nd International Conference on data Science (ICDS)

作者： Cui, Limeng Qi, Zhiquan Chen, Zhensong Meng, Fan Shi, Yong Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictious Econ & Data Sci Beijing 100190 Peoples R China UCAS Sch Comp & Control Engn Beijing 100190 Peoples R China UCAS Sch Management Beijing 100190 Peoples R China

ISBN: (数字)9783319244747

ISBN: (纸本)9783319244747;9783319244730

Pavement distress detection is a key technology to evaluate pavement surface and crack severity. However, there are many challenging problems when using pavement distress detection technology to do road maintenance, such as the inference of textured surroundings with similar intensity to the distresses, the existence of intensity inhomogeneity along the distresses and the requirement of real-time detection in practice. To address these problems, we propose a novel method for pavement distress detection based on random decision forests. By introducing the color gradient features at multiple scales commonly used in contour detection, we extend the feature set of traditional distress detection methods and get the represented crack with richer information. During the process of training, we apply a subsampling strategy at each node to maintain the diversity of trees. With this work, we finally solve all the three problems mentioned above. In addition, according to the characteristics of random decision forests, our method is easy to parallel and able to conduct real-time detection. Experimental results show that our approach is faster and more accurate than existing methods.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Market value of Innovation: An empirical analysis on China's stock market 3

Market value of Innovation: An empirical analysis on China's...

引用

3rd International Conference on Information Technology and Quantitative management (ITQM)

作者： Meng Xiangying Zhang Yueyan Wei Xianhua Univ Chinese Acad Sci Sch Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100864 Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Tsinghua Univ Dept Automot Engn Beijing 100084 Peoples R China

Studies on market value of innovation investment have been based on US and European database. However, few researchers investigate Chinese stock market about the market value of firm's expected R&D performance. In this paper, we use constructed panel dataset from three representative stock markets in China: Main Board Market, Small and Media-sized Enterprise Board Market and Growth Enterprise Market and then use intangible assets increment as innovation indicator to examine the innovation investment-firm performance association in both manufacturing and service industries by a dataset over an 11-year period covering 1455 firms to evaluate the differences in their relative contribution to market performance. By comparing the results of these three stock markets, we find out the effect of R&D investment and increasing R&D input to market value is insignificant in all three stock markets, resulting from the weak protection for minority investors and loose regulation of information disclosure. Different constructions of intangible assets of listed firms on MBM and GEM account for market's relatively low efficiency to reflect real value of R&D investment. On industry-level, we conclude that R&D investment in both service and manufacturing sector contributes positively to market performance of firms and R&D investment in service industry shows stronger and more significant linkage to market value than manufacturing industry. (C) 2015 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Market value Innovation R&D China's stock market

来源：评论

学校读者我要写书评

暂无评论

Connected Bit Minwise Hashing for Large-Scale Linear SVM 12

Connected Bit Minwise Hashing for Large-Scale Linear SVM

引用

12th International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Tang, Jingjing Tian, Yingjie Liu, Dalian Univ Chinese Acad Sci Sch Math Sci Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Res Ctr Fictitious Econ & Data Sci Beijing Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing Peoples R China

ISBN: (纸本)9781467376822

In this paper, we propose to integrate linear SVM with connected bit minwise hashing to improve the training and testing efficiency practically without notable loss of accuracy. Although the resemblance kernel is nonlinear and appears not directly to be used for linear SVM, our proof of positive definiteness of the connected bit minwise hashing kernel provide a reasonable and effective foundation for integration, which merely requires a minor modification of LIBLINEAR and the original b-bit minwise hashing scheme. Moreover, connected bit is convenient to be constructed and the performance increases preferably with a far-reaching practical significance in the large-scale data environment. Theoretical analysis and experimental results illustrate the effectiveness of this algorithm.

关键词： minwise hashing connected bit similarity estimation large-scale linear SVM

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：