检索结果-内蒙古大学图书馆

The 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA 2015)

作者： Xuehua Zhao Xueyan Liu Daoliang Li Huiling Chen Shuangyin Liu Xinbin Yang Shaobin Zhan Wenyong Zhao School of Digital Media Shenzhen Institute of Information Technology Key Laboratory of Symbolic Computation and Knowledge Engineer (Jilin University) Ministry of Education College of Information and Electrical Engineering China Agricultural University College of Physics and Electronic Information Wenzhou University College of Information Guangdong Ocean University

ISBN: (纸本)9783319483566

The excellent feature set or feature combination of cotton foreign fibers is great significant to improve the performance of machine-vision-based recognition system of cotton foreign fibers. To find the excellent feature sets offoreign fibers, in this paper presents three metaheuristic-based feature selection approaches for cotton foreign fibers recognition, which are particle swarm optimization, ant colony optimization and genetic algorithm, respectively. The k-nearest neighbor classifier and support vector machine classifier with k-fold cross validation are used to evaluate the quality offeature subset and identify the cotton foreign fibers. The results show that the metaheuristic-based feature selection methods can efficiently find the optimal feature sets consisting of a few features. It is highly significant to improve the performance of recognition system for cotton foreign fibers.

关键词： Metaheuristic Feature selection Foreign fibers Recognition system Feature extraction recognition system Fiber Fibrous materials Cotton Support Vector Network

来源：评论

学校读者我要写书评

暂无评论

Predicting user influence based on improved linear threshold model in social networks

引用

Journal of computational Information Systems 2014年第14期10卷 6151-6160页

作者： Tian, Zhongsheng Wang, Ying Zhao, Qiuyue Zuo, Wanli College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Lab. of Symbolic Computation and Knowledge Engineering Attached to the Ministry of Education Changchun China

With the scale of social networks growing rapidly, the amount of user participating in it increases at astonishing speed. Predicting user influence in social networks is an interesting and useful research direction. There are lots of works on this area, mainly covering the following aspects: Maximize Influence, Influence Diffusion, Predicting Influential Users and Predicting social influence. The common issue of above aspects is that they can't calculate the precise user influence. In this work, we propose a novel method to give each user influence a precise value in social networks. The proposed method is based on Improved Linear Threshold Model (ILTM). To build ILTM, we take a series of measures to overcome the deficiencies brought by Linear Threshold Model, such as assigning random threshold to node, cold start. So we adopt user similarity instead of random threshold and utilize cluster algorithm and link analysis algorithm to surmount cold start. © 2014 by Binary Information Press

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Implicit knowledge discovery in biomedical ontologies: Computing interesting relatednesses

Implicit knowledge discovery in biomedical ontologies: Compu...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian Bai Leiguang Gong Casimir A. Kulikowski Lan Huang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Yantai Intelligent Information Technologies Ltd. Yantai China Department of Computer Science Rutgers The State University of New Jersey Piscataway NJ USA

Ontologies, seen as effective representations for sharing and reusing knowledge, have become increasingly important in biomedicine, usually focusing on taxonomic knowledge specific to a subject. Efforts have been made to uncover implicit knowledge within large biomedical ontologies by exploring semantic similarity and relatedness between concepts. However, much less attention has been paid to another potentially helpful approach: discovering implicit knowledge across multiple ontologies of different types, such as disease ontologies, symptom ontologies, and gene ontologies. In this paper, we propose a unified approach to the problem of ontology based implicit knowledge discovery - a Multi-Ontology Relatedness Model (MORM), which includes the formation of multiple related ontologies, a relatedness network and a formal inference mechanism based on set-theoretic operations. Experiments for biomedical applications have been carried out, and preliminary results show the potential value of the proposed approach for biomedical knowledge discovery.

关键词： Biological system modeling Biomedical measurement Ontologies Arthritis Diseases

来源：评论

学校读者我要写书评

暂无评论

A new epistasis detecting algorithm based on ant colony optimization 14

A new epistasis detecting algorithm based on ant colony opti...

引用

6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014

作者： Zhou, Zhihui Liu, Guixia Su, Lingtao Han, Liang Yan, Lun College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)9781450328104

The rapid developments of chip-based technology have greatly improved human genetics and made routine the access of thousands of single nucleotide polymorphisms (SNPs) contributing to an informatics challenge. The characterization and interpretation of genes and gene-gene interactions that affect the susceptibility of common, complex multifactorial diseases is a computational and statistical challenge in genome-wide association studies (GWAS). Various methods have been proposed, but they have dificulty to be directly applied to GWAS caused by excessive search space and intensive computational burden. In this paper, we propose an ant colony optimization (ACO) based algorithm by combining the pheromone updating rule with the heuristic information. We tested power performance of our algorithm by conducting suficient experiments including a wide range of simulated datasets experiments and a real genome-wide dataset experiment. Experimental results demonstrate that our algorithm is time efficient and gain good performance in the term of the power of prediction accuracy. Copyright 2014 ACM.

关键词： Ant colony optimization

来源：评论

学校读者我要写书评

暂无评论

Detecting epistasis by LASSO-penalized-model search algorithm in human Genome-Wide Association Studies

Detecting epistasis by LASSO-penalized-model search algorith...

引用

4th International Conference on Materials Science and Information Technology, MSIT 2014

作者： Zhou, Zhi Hui Liu, Gui Xia Su, Ling Tao Han, Liang Yan, Lun College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)9783038351733

Extensive studies have shown that many complex diseases are influenced by interaction of certain genes, while due to the limitations and drawbacks of adopting logistic regression (LR) to detect epistasis in human Genome-Wide Association Studies (GWAS), we propose a new method named LASSO-penalized-model search algorithm (LPMA) by restricting it to a tuning constant and combining it with a penalization of the L1-norm of the complexity parameter, and it is implemented utilizing the idea of multi-step strategy. LASSO penalized regression particularly shows advantageous properties when the number of factors far exceeds the number of samples. We compare the performance of LPMA with its competitors. Through simulated data experiments, LPMA performs better regarding to the identification of epistasis and prediction accuracy. © (2014) Trans Tech Publications, Switzerland.

关键词： Genes

来源：评论

学校读者我要写书评

暂无评论

Boosting image retrieving result with GPS sensor information

引用

Sensor Letters 2014年第2期12卷 213-217页

作者： Zhao, Hongwei Wang, Zhen Liu, Pingping Gao, Shan Nong, Zhimeng China School of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education No. 2699 Qianjin Street Chaoyang District Changchun China

Due to the limited computing resource on mobile devices, it is more difficult to get semantically relevant results from a large dataset in time in a mobile image retrieving system, compared with normal content based image retrieval system, such as in a PC. In order to fix up this problem, images annotated with GPS sensor information have been proposed in this *** an image is acquired by a mobile device, the corresponding location information deriving from GPS coordinates can be obtained by the GPS sensor embedded in the mobile device. For the purpose of using the GPS information, the index database should be built first. In such an indexed database, the whole image database is divided into several small independent databases according to images' location label. Depending on this indexed database and the location label of the query image, image retrieval is carried in a small dataset. To further accelerate the retrieval speed, the hash-based image feature is adopted in this paper. Experimental results show that our proposed method has excellent retrieval performance and low computational complexity. Copyright © 2014 American Scientific Publishers.

关键词： Global positioning system

来源：评论

学校读者我要写书评

暂无评论

An object shrunken algorithm for image classification

引用

Journal of computational Information Systems 2014年第20期10卷 8741-8748页

作者： Pan, Zheng Ouyang, Dantong College of Computer Science and Technology Jilin University ChangChun China Key Laboratory of Computation and Knowledge Engineering Ministry of Education ChangChun China

In this letter, we suggest a novel Object Shrunken (OS) algorithm to handle the image classification task. Unlike the prior art, this letter considers the foreground or the object location in the image for more discriminative image-level representation. The OS algorithm suggests a straightforward procedure to box the object location. It first proposes a Weighted Local Outlier Factor (WLOF) to remove all the interest point outliers, and then positions the object location in terms of the distribution of the rest interest points. We evaluate the proposed algorithm on the well-known dataset Caltech-101. The resulting OS algorithm outperforms the state-of-art approaches in the image classification task.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Video Sequence-Based Iris Recognition Inspired by Human Cognition Manner

引用

Journal of Bionic engineering 2014年第3期11卷 481-489页

作者： Yuanning Liu College of Computer Seience and Technology Jilin University Changchun 130012 P. R. China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 P. R. China College of Software Nanchang HangKong University Nanchang 330063 P. R. China

In video sequence-based iris recognition system, the problem of making full use of relationship and correlation among frames still remains to be solved. A brand new template level multimodal fusion algorithm inspired by human cognition manner is proposed. In that a non-isolated geometrical manifold, named Hyper Sausage Chain due to its sausage shape, is trained using the frames from a pattern class for representing an iris class in feature space. We can classify any input iris by observing which manifold it locates in. This process is closer to the function of human being, which takes ＇matter cognition＇ instead of ＇matter classification＇ as its basic principle. The experiments on self-developed JLUBR-IRIS dataset with several video sequences per person demonstrate the effectiveness and usability of the proposed algorithm for video sequence-based iris recognition. Fur- thermore, the comparative experiments on public CASIA-I and CASIA-V4-Interval datasets show that our method can also achieve improved performance of image-based iris recognition system, provided enough samples are involved in training stage.

关键词： iris recognition video sequences multi-modal fusion bionic recognition hyper sausage neuron

来源：评论

学校读者我要写书评

暂无评论

Recognizing event in short text based on decision tree

Recognizing event in short text based on decision tree

引用

作者： Lu, Jing Ya Zuo, Wan Li Zhu, Liang College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education of China China

ISBN: (纸本)9783038351399

Mining newsworthy events from a large number of microblogging information is not only the primary problem that several big microblogging websites need to solve, but also a new research field in micro-information age. For now, a lot of study about even recognizing has been made at home and abroad, but relatively rarely contrapose short text(microblogging message). The paper considers newsworthy event recognizing in short text as classification problem, utilizes the decision tree classification algorithm in data mining, sufficiently mines features of event in short text, and then recognizes the newsworthy event in microblogging. In the last, we verify the effect of the model. © (2014) Trans Tech Publications, Switzerland.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Efficiently and fast learning a fine-grained stochastic blockmodel from large networks

Efficiently and fast learning a fine-grained stochastic bloc...

引用

18th Pacific-Asia Conference on Advances in knowledge Discovery and Data Mining, PAKDD 2014

作者： Zhao, Xuehua Yang, Bo Chen, Hechang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineer Ministry of Education Jilin University Changchun China

ISBN: (纸本)9783319066073

Stochastic blockmodel (SBM) has recently come into the spotlight in the domains of social network analysis and statistical machine learning, as it enables us to decompose and then analyze an exploratory network without knowing any priori information about its intrinsic structure. However, the prohibitive computational cost limits SBM learning algorithm with the capability of model selection to small network with hundreds of nodes. This paper presents a fine-gained SBM and its fast learning algorithm, named FSL, which ingeniously combines the component-wise EM (CEM) algorithm and minimum message length (MML) together to achieve the parallel learning of parameter estimation and model evaluation. The FSL significantly reduces the time complexity of the learning algorithm, and scales to network with thousands of nodes. The experimental results indicate that the FSL can achieve the best tradeoff between effectiveness and efficiency through greatly reducing learning time while preserving competitive learning accuracy. Moreover, it is noteworthy that our proposed method shows its excellent generalization ability through the application of link prediction. © 2014 Springer International Publishing.

关键词： Learning algorithms

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：