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International Conference on Computer and Information Technology (CIT)

作者： Xiaoxin Guo Zhiwen Xu Yinan Lu Yunjie Pang Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)076952432X

Fourier-Mellin transform (FMT) is frequently used in content-based image retrieval and digital image watermarking. This paper extends the application of FMT into image registration and proposes an improved registration algorithm based on FMT for the alignment of images differing in translation, rotation angle, and uniform scale factor. The proposed algorithm can eliminate the conversion from Cartesian to log-polar coordinates, avoid the process of interpolation required in the conversion, and obtain a more significant improvement than the conventional method using cross-correlation. Experiments show that the algorithm is accurate and robust regardless of white noise

关键词： Fourier transforms Image registration Image converters Image retrieval Content based retrieval Digital images Watermarking Interpolation Noise robustness White noise

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Qualitative spatial relationships cleaning for spatial data mining

Qualitative spatial relationships cleaning for spatial data ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hai-Bin Sun Wen-Hui Li Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China

In this article, we investigate the problem of preparing qualitative spatial relations before implementing spatial data mining by checking consistency in a constraint network, which includes topological and cardinal directional relations between pairs of spatial objects. We aim to explore potential spatial relations and possible inconsistency among the data of relationships for enforcing the correctness of spatial data mining. This task is carried out through qualitative spatial reasoning method, specifically consistency checking. We try to lay the theoretical foundation for this kind of problem. Instead of using conventional composition tables, we investigate the interactions between topological and cardinal directional relations with the aid of rules. These rules are shown to be sound, i.e. the deductions are logically correct. Based on these rules, an improved constraint propagation algorithm is introduced to enforce the path consistency. An example is presented to show the utility of these rules.

关键词： Cleaning Data mining computational complexity Spatial databases Topology Sun Laboratories Computer networks knowledge engineering Computer science education

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Robust estimation of the fundamental matrix based on an error model

Robust estimation of the fundamental matrix based on an erro...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hui-Xiang Zhong Yue-Ping Feng Yun-Jie Pang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

A new method is presented for robustly estimating fundamental matrix from matched points. The method comprises two parts. The first uses a robust technique - the random sample consensus (RANSAC) to discard outliers in an initial set of matched points. It adopts the sampling strategy to generate inliers from the initial set. The second part of the method is an algorithm for computing fundamental matrix, using the output of the RANSAC. This algorithm is based on the consistent fundamental matrix estimation in a quadratic measurement error model. An extended system for determining the estimator is proposed, and an efficient implementation for solving the system - a continuation method is developed. The proposed algorithm avoids solving total eigenvalue problems. Results for both synthetic and real images show the effectiveness of the proposed method.

关键词： Robustness Measurement errors Computer errors Eigenvalues and eigenfunctions Computer vision Layout Educational institutions Computer science Educational technology Laboratories

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Medicine composition concentration analysis based on least square support vector machine

Medicine composition concentration analysis based on least s...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Guo, Xin-Chen Chen, Zhou-Yi Teng, Li-Rong Wu, Chun-Guo Du, Tian-Bao Lu, Jia-Hui Meng, Qing-Fan Liang, Yan-Chun Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Science NortheastChinaInstituteofElectricPowerEngineering Jilin 132012 China Institute of General Development and Evaluation of Equipment EAAF PLA Beijing 100076 China College of Life Science Jilin University Changchun 130012 China

ISBN: (纸本)078039092X

This paper presents a method of medicine composition concentration analysis based on least square support vector machines (LS-SVMs) and examines the importance of the hyperparameter choice in improvement of algorithm performance. Simulation results show that the proposed method obtains high quality precision in the generalization, compared with multiple linear regression, and that it is an efficient approach to regression estimation. © 2005 IEEE.

关键词： Medicine

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AN IMPROVED BICLUSTERING ALGORITHM AND ITS APPLICATION TO GENE EXPRESSION SPECTRUM ANALYSIS

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Genomics, Proteomics & Bioinformatics 2005年第3期3卷 189-193页

作者： Hua Qu Liu-Pu Wang Yan-Chun Liang Chun-Guo Wu College of Software Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun 130012 China Guangzhou Institute of China Telecom Guangzhou 510630 China College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China Key Laboratory of Information Science and Engineering of the Ministry of Railway/Key Laboratory of Advanced Information Science and Network Technology of Beijing Beijing Jiaotong University Beijing 100044 China.

Cheng and Church algorithm is an important approach in biclustering algorithms. In this paper, the process of the extended space in the second stage of Cheng and Church algorithm is improved and the selections of two important parameters are discussed. The results of the improved algorithm used in the gene expression spectrum analysis show that, compared with Cheng and Church algorithm, the quality of clustering results is enhanced obviously, the mining expression models are better, and the data possess a strong consistency with fluctuation on the condition while the computational time does not increase significantly.

关键词： biclustering algorithm gene expression pedigree analysis Cheng and Church algorithm

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Nondestructive quantitative analysis of compound paracetamol and diphenhydramine hydrochloride powder using RBF networks

Nondestructive quantitative analysis of compound paracetamol...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Zheng, Hong Wan, Li.-Ming Jiang, Jing-Qing Liang, Yan-Chun Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Computer Science and Engineering Changchun University of Technology Changchun 130012 China Institute of General Development and Evaluation of Equipment EAAF of PLA Beijing 100076 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China

ISBN: (纸本)078039092X

A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drugs on near-infrared (NIR) spectroscopy is developed by using a Radial Basis Function (RBF) network. Nearest neighbor-clustering algorithm is used as the learning algorithm of RBF network. Comparisons of the results obtained from the RBF models with those from BP models show that it is feasible to use the RBF network in nondestructive quantitative analysis of the components of drugs. © 2005 IEEE.

关键词： Neural networks

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Incremental algorithm for detecting community structure in dynamic networks

Incremental algorithm for detecting community structure in d...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Bo Yang Da-You Liu Open Symbol Computation and Knowledge Engineering Laboratory of State Education Department Jilin University Changchun China Jilin University Changchun Jilin CN Coll. of Comput. Sci. & Technol. Jilin Univ. Changchun China

Community structure is an important property of networks. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However all these algorithms can work well only with small or moderate networks with vertexes of order 10/sup 4/. Additionally, all existing algorithms are off-line and can not work well with highly dynamic networks such as Web, in which Web pages are updated frequently every day. When the already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even only slight changes involved. To address this problem we develop an incremental algorithm which allows for detecting community structure in large-scale and dynamic networks. Based on the community structure it detect previously our algorithm takes little time to reclassify the entire network including both original and incremental parts. Furthermore, our algorithm is faster than most existing algorithms such as Girvan and Newman's algorithm and its improved versions. Also our algorithm can help to visualize these community structures in network and provide a new approach to research the evolving process of dynamic networks.

关键词： Intelligent networks Clustering algorithms Partitioning algorithms Social network services Machine learning algorithms Computer networks Visualization Web sites Biochemistry Educational institutions

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Medicine composition concentration analysis based on least square support vector machine

Medicine composition concentration analysis based on least s...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Xin-Chen Guo Zhou-Yi Chen Li-Rong Teng Chun-Guo Wu Tian-Bao Du Jia-Huilu Qing-Fan Meng Yan-Chun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun China College of Science Northeast China Institute of Electric Power Engineering Jilin China EAAF of PLA Institute of General Development and Evaluation of Equipment Beijing China College of Life Science Jilin University Changchun China

关键词： Least squares methods Support vector machines Educational institutions Performance analysis Algorithm design and analysis knowledge engineering Linear regression Spectral analysis Quadratic programming Lagrangian functions

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Approximated non-tensor pre-wavelets neural network based on compound training algorithm

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Journal of Information and computational Science 2005年第4期2卷 715-722页

作者： Li, Ying Deng, Zhidong Zou, Xi Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 China Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130021 China Institute of Mathematics Jilin University Changchun 130021 China

In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear weights of APWNN are trained by the self-adaptive learning rate method. On the other hand an extended Kalman filter method is used to update the nonlinear parameters such as dilation parameters and translation parameters. Additionally we demonstrate the efficiency of our proposed method through a concrete example of function approximation.

关键词： Neural networks

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Nondestructive quantitative analysis of compound paracetamol and diphenhydramine hydrochloride powder using RBF networks

Nondestructive quantitative analysis of compound paracetamol...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hong Zheng Li-Ming Wan Jing-Qing Jiang Yan-Chun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China College of Computer Science and Engineering Changchun University of Technology Changchun China Institute of General Development and Evaluation of Equipment EAAF of PLA Beijing China College of Mathematics and Computer Science Inner Mongolia University for the Nationalities Tongliao China

A method for simultaneous analysis of the two components of compound paracetamol and diphenhydramine hydrochloride powdered drugs on near-infrared (NIR) spectroscopy is developed by using a radial basis function (RBF) network. Nearest neighbor-clustering algorithm is used as the learning algorithm of RBF network. Comparisons of the results obtained from the RBF models with those from BP models show that it is feasible to use the RBF network in nondestructive quantitative analysis of the components of drugs.

关键词： Powders Radial basis function networks Clustering algorithms Educational institutions Computer science Drugs Spectroscopy Educational technology knowledge engineering Artificial neural networks

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