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NCM 2009 - 5th International Joint Conference on Int. Conf. on Networked Computing, Int. Conf. on Advanced Information Management and Service, and Int. Conf. on Digital Content, Multimedia technology and its Applications

作者： Gang, Kou Yi, Peng School of Management and Economics University of Electronic Science and Technology of China Chengdu 610054 China CAS Research Center on Fictitious Economy and Data Sciences Beijing 100080 China

ISBN: (纸本)9780769537696

The advances in digital data collection and storage technologies during the last two decades allow companies and organizations store up huge amounts of electronic documents. Large collections of electronic text present opportunities and challenges. How to assist users to find the most relevant documents from vast text collections efficiently is one of the challenges. This study proposes a hierarchical clustering method to efficiently label documents that satisfy users' information needs. An experiment was conducted to examine the proposed method and the results shown that the clustering method is effective and efficient, in terms of both objective and subjective measures. © 2009 IEEE.

关键词： Information retrieval

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C3M: A classification model for multivariate motion time series

C3M: A classification model for multivariate motion time ser...

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2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009

作者： Dengyuan, Wu Ying, Liu Ge, Gao Zhendong, Mao Tao, He Graduate University of Chinese Academy of Sciences China Instittue of Computing Technology CAS China University of Virginia United States Beijing Zhongke Fulong Computer Technology Co. Ltd. China Fictitious Economy and Data Science Research Center CAS China

ISBN: (纸本)9780769535074

The problem of time series classification has drawn intensive attention from the data mining community. Conventional time series model may be unsuitable for multivariate motion time series because of the large volume of the data, highly correlated dimensions and rapid growth nature. In this paper, we propose C3M, an effective classification model for motion time series classification, which consists of segmentation, dimension ranking and selection, and classification. We propose new segmentation and dimension selection scheme that reduce the storage volume but keep enough valuable information and correlation between different dimensions. Experimental results show that C3M achieves significant performance improvements in terms of both classification accuracy and execution time over conventional schemas. © 2008 IEEE.

关键词： Time series

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Kernel-based nonparametric regression method

Kernel-based nonparametric regression method

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2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent technology - Workshops, WI-IAT Workshops 2008

作者： Zhang, Dongling Tian, Yingjie Zhang, Peng Research Center on Fictitious Economy and Data Science CAS Beijing 100080 China Beijing University of Science and Technology Beijing 100083 China Graduate University Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9780769534961

Regression is a basic statistical tool in data mining, which is to predict the relationship between a dependent variable and one or more independent variables. Parametric and nonparametric regression are two kinds of regression approach used for various problems. In this paper, we proposed a kernel-based nonparametric regression method, which can solve nonlinear regression problem properly by mapping the data to a higher-dimensional space by kernel function. With this method, we conducted a series of experiments on nonlinear function and real world regression problems, and the results reveal the effectiveness of the model. © 2008 IEEE.

关键词： data mining

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The Global Convergence of Self-Scaling BFGS Algorithm with Nonmonotone Line Search for Unconstrained Nonconvex Optimization Problems

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Acta Mathematica Sinica,English Series 2007年第7期23卷 1233-1240页

作者： Hong Xia YIN Dong Lei DU Chinese Academy of Sciences Research Center on Data Technology and Knowledge Economy Department of Mathematics Graduate University of the Chinese Academy of Sciences Faculty of Administration University of New Brunswick

The self-scaling quasi-Newton method solves an unconstrained optimization problem by scaling the Hessian approximation matrix before it is updated at each iteration to avoid the possible large eigenvalues in the Hessian approximation matrices of the objective function. It has been proved in the literature that this method has the global and superlinear convergence when the objective function is convex （or even uniformly convex）. We propose to solve unconstrained nonconvex optimization problems by a self-scaling BFGS algorithm with nonmonotone linear search. Nonmonotone line search has been recognized in numerical practices as a competitive approach for solving large-scale nonlinear problems. We consider two different nonmonotone line search forms and study the global convergence of these nonmonotone self-scale BFGS algorithms. We prove that, under some weaker condition than that in the literature, both forms of the self-scaling BFGS algorithm are globally convergent for unconstrained nonconvex optimization problems.

关键词： nonmonotone line search self-scaling BFGS method global convergence

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Study on process-oriented intelligent knowledge management Meta-Synthesis system model

Study on process-oriented intelligent knowledge management M...

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2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007

作者： Lingling, Zhang Yang, Song Jun, Li School of Management Graduate University Chinese Academy of Sciences Beijing China CAS Research Center on Data Technology and Knowledge Economy Beijing China

ISBN: (纸本)1424413125

In economic era, knowledge has become the first resource of enterprise. So how to acquire knowledge and share it have become a very important topic. Process-oriented knowledge management (POKM) is a completely new and practical valuable research field. It may be the most practical solution to solve the knowledge management difficulties. This paper analyzes background and the hypothesis of the POKM, and proposes a POKM model. The definition of flow knowledge is promoted in this paper. It also discusses a Process-oriented intelligent knowledge management Meta-Synthesis system model. This model can integrate knowledge, process and human. It can improve knowledge of employee and enhance decision support and adaptability of the process. © 2007 IEEE.

关键词： knowledge management

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Unsupervised and semi-supervised Lagrangian support vector machines

Unsupervised and semi-supervised Lagrangian support vector m...

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7th International Conference on Computational Science (ICCS 2007)

作者： Zhao, Kun Tian, Ying-Jie Deng, Nai-Yang College of Science China Agricultural University Chinese Academy of Sciences Research Center on Data Technology and Knowledge Economy

ISBN: (纸本)9783540725879

Support Vector Machines have been a dominant learning technique for almost ten years, moreover they have been applied to supervised learning problems. Recently two-class unsupervised and semi-supervised classification problems based on Bounded C-Support Vector Machines and Bounded v-Support Vector Machines are relaxed to semi-definite programming[4][11]. In this paper we will present another version to unsupervised and semi-supervised classification problems based on Lagrangian Support Vector Machines, which trained by convex relaxation of the training criterion: find a labelling that yield a maximum margin on the training data. But the problems have difficulty to compute, we will find their semi-definite relaxations that can approximate them well. Experimental results show that our new unsupervised and semi-supervised classification algorithms often obtain almost the same accurate results as the unsupervised and semi-supervised methods [4][11], while considerably faster than them.

关键词： Lagrangian Support Vector Machines semi-definite programming unsupervised learning semi-supervised learning margin

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Web-service-based crop simulation system: A case study of geo-service in crop simulation system 4

Web-service-based crop simulation system: A case study of ge...

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4th International Symposium on Intelligent Information technology in Agriculture, ISIITA 2007

作者： Jian, Wang Zhiqiang, Wang Gan, Guohui Xu, Yong Ma, Guoxia National Engineering Research Center for Information Technology in Agriculture Beijing China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China Research Center on Data Technology and Knowledge Economy CAS Beijing China

Applying web service technology into the crop simulation system, the author brings forward an integrated system framework established on the basis of web service and GIS the author also accomplished the web service to simulate crop yield and developed a system under the framework successfully, which has been applied into the Yangou basin in China the practice shows that it is feasible to put the web service into the use of crop simulation, which is in favor of system integration and share of the crop models for the whole society..

关键词： Web services

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A leave-one-out bound for v - Support vector regression

A leave-one-out bound for v - Support vector regression

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7th International Conference on Computational Science, ICCS 2007

作者： Ruxin, Qin Jing, Chen Naiyang, Deng Yingjie, Tian College of Science China Agricultural University 100083 Beijing China Research Center on Data Technology and Knowledge Economy Chinese Academy of Sciences 100080 Beijing China

ISBN: (纸本)9783540725879

An upper bound on the Leave-one-out (Loo) error for v - support vector regression [v - SVR) is presented. This bound is based on the geometrical concept of span. We can select the parameters of v - SVR by minimizing this upper bound instead of the error itself, because the computation of the Loo error is extremely time consuming. We also can estimate the generalization performance of v - SVR with the help of the upper bound. It is shown that the bound presented herein provide informative and efficient approximations of the generalization behavior based on two data sets. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Branch and bound method

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Unconstrained Transductive Support Vector Machines

Unconstrained Transductive Support Vector Machines

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Yingjie Tian Manfu Yan Chinese Academy of Sciences Research Center Data Technology and Knowledge Economy Tarbiat Moalem University Iran

Support vector machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on transductive support vector machines (TSVM) for classification and construct a new algorithm - unconstrained transductive support vector machines (UTSVM). After researching on the special construction of primal problem in TSVM, we transform it to an unconstrained problem and then smooth the derived problem in order to apply usual optimization methods.

关键词： Support vector machines Support vector machine classification Static VAr compensators Predictive models Educational institutions Machine learning Machine learning algorithms Optimization methods Artificial intelligence Medical services

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SVM-based Automatic Classification for Protein Structural Domain

SVM-based Automatic Classification for Protein Structural Do...

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第一届最优化与系统生物学国际研讨会

作者： Xiao-Jian Shao Ying-Jie Tian Nai-Yang Deng College of Science China Agricultural University Chinese Academy of Sciences Research Center on Data Technology and Knowledge Economy

The automatic classification for protein structure plays an important role in bioinformatics. Here we present an improved multiclass SVM for the classification based on the features of the protein structure which were extracted from the protein convex hull. Firstly, we modify the gauss radial kernel by adding a positive constant to the kernel function. Secondly, we take weighted SVM to deal with the imbalanced dataset. Experiments demonstrate the superiority of our new strategies. In addition, we design the hierarchical classifier which is more suitable to the CATH database.

关键词： Protein Structure Support Vector Machines Hierarchical Classification.

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