检索结果-内蒙古大学图书馆

Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems

作者： Zhang, Huijie Sun, Jigui Liu, Jin Lv, Nan College of Computer Science and Technology Jilin University NorthEast Normal University Changchun China College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering Changchun China College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781595939142

In this paper, we put forward a fast simplification method for the terrain model by integrating the discrete particle swarm optimization with the hierarchical structure. In this method, each particle is represented as a hierarchical structure and corresponds to a candidate solution of the simplified terrain model. In order to acquire an optimal simplified model, we proposed a new model error evaluation function based on the terrain features. Thereby the terrain model yielded by this method has high self-adaptability and optimal simplification ratio. Furthermore, the velocity of the particle is redefined as the probability of the model being simplified superiorly. Therefore, the particles can converge to the optimal simplified model rapidly. The experimental results performed on benchmark terrain data show that the efficiency and the precision of our algorithm are improved greatly and the detailed features in the simplified model are preserved, compared with the others common simplification method. © 2007 ACM.

关键词： Landforms

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A Method of Gene-Function Annotation Based on Variable Precision Rough Sets

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Journal of Bionic engineering 2007年第3期4卷 177-184页

作者： Zhi-li Pei Xiao-hu Shi Meng Niu Xu-ning Tang Li-sha Liu Ying Kong Yan-chun Liang College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 P. R. China College of Mathematics and Computer Science Inner Mongolia University for NationalitiesTongliao 028043 P. R. China College of Software Jilin University Changchun 130012 P. R. China Department of Biochemistry and Molecular Biology Institution of Glycobiology Dalian Medical University Dalian 116027 P. R. China

It is very important in the field of bioinformatics to apply computer to perform the function annotation for new sequenced bio-sequences. Based on GO database and BLAST program, a novel method for the function annotation of new biological sequences is presented by using the variable-precision rough set theory. The proposed method is applied to the real data in GO database to examine its effectiveness. Numerical results show that the proposed method has better precision, recall-rate and harmonic mean value compared with existing methods.

关键词： gene function annotation variable precision rough set GO BLAST

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SVM-FastICA based detection ensemble system of EEG

SVM-FastICA based detection ensemble system of EEG

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2nd International Conference on Convergent Information Technology, ICCIT 07

作者： Jindong, Zhang Guine, Qin Yue, Cui Jinnan, Dong Lishu, Guo Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University 130012 Changchun China State Key Laboratory of Automobile Dynamics Simulation Jilin University 130025 Changchun China Zhejiang Asia-Pacific Mechanical Andelectric Co. Ltd. 311023 Hangzhou China

ISBN: (纸本)0769530389

An EEG signal detection ensemble system to solve the low rate of vision detection is developed when analysis so many EEG signals. A novel FastICA method is presented, in which the independent component analysis approach is used to acquire the high order statistic information of EEG intrusion action mode and mapped the input mode space into the corresponding independent component space. Then the generalized maximal margin hyperplane is constructed in the independent component space using the support vector machine. Testing results show that the system integrates the features of FastICA and SVM to response real-time and lower the rate of false negative. © 2007 IEEE.

关键词： Support vector machines

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A populations evolution study using the genotype frequency data of single nucleotide polymorphism from Y-chromosome

A populations evolution study using the genotype frequency d...

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2007 1st International Conference on Bioinformatics and Biomedical engineering, ICBBE

作者： Pei, Zhili Kong, Ying Shi, Xiaohu Lian, Zhichao Tang, Xuning Liu, Lisha Cao, Zhongbo Liang, Yanchun Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Chang'chun 130012 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tong'liao 028043 China Department of Biochemistry and Molecular Biology Institution of Glycobiology Dalian Medical University Da'lian 116027 China

ISBN: (纸本)1424411203

This paper proposes a novel way for the population evolution research. In the paper, it constructs the phylogenies tree of four populations using the single nucleotide polymorphism (SNP) genotype frequency of human's Y-chromosome provided by the international Hapmap project firstly, then a mathematical model is proposed to calculate the distance of any two races for all SNP genotype frequency. The simulation results suggest that the theory of constructing phylogenetic tree by analyzing SNP genotype frequency is effective. The accuracy of the adopted processing method is also shown by the simulation results. About the origination of modern human being, the result of this study can support the 'out of Africa' hypothesis, and this result is same as most evidences of paleontology. © 2007 IEEE.

关键词： Gene expression

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Effective pruning algorithm for least squares support vector machine classifier

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2007年第7期44卷 1128-1136页

作者： Yang, Xiaowei Lu, Jie Zhang, Guangquan School of Mathematical Sciences South China University of Technology Guangzhou 510641 China Faculty of Information Technology Sydney University of Technology NSW 2007 Australia Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun 130012 China

A well-known drawback in the least squares support vector machine (LS-SVM) is that the sparseness is lost. In this study, an effective pruning algorithm is developed to deal with this problem. To avoid solving the primal set of linear equations, the bottom to the top strategy is adopted in the proposed algorithm. During the training process of the algorithm, the chunking incremental and decremental learning procedures are used alternately. A small support vector set, which can cover most of the information in the training set, can be formed adaptively. Using the support vector set, one can construct the final classifier. In order to test the validation of the proposed algorithm, it has been applied to five benchmarking UCI datasets. In order to show the relationships among the chunking size, the number of support vector machine, the training time, and the testing accuracy, different chunking sizes are tested. The experimental results show that the proposed algorithm can adaptively obtain the sparse solutions without almost losing generalization performance when the chunking size is equal to 2, and also its training speed is much faster than that of the sequential minimal optimization (SMO) algorithm. The proposed algorithm can also be applied to the least squares support vector regression machine as well as LS-SVM classifier.

关键词： Support vector machines

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Automated reasoning expanded method based on set signs

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2007年第8期44卷 1317-1323页

作者： Liu, Quan Fu, Yuchen Sun, Jigui Cui, Zhiming Gong, Shengrong Ling, Xinghong Institute of Computer Science and Technology Soochow University Suzhou 215006 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China Laboratory of Symbol Calculation and Knowledge Engineering Jilin University Changchun 130012 China

On the basis of the many-valued logics tableau reasoning, an automated reasoning expansion method based on set sign is presented. This approach which treats set signs as truth values can be applied to some methods and techniques of reasoning suited to classical logics, which make reasoning reform from the non-classical logics to the classical logics. Through rewriting set signs and expansion rules, it is very easy to spread to modal logics, intuitionistic logics and so on. The technology can also be further spread to infinite-valued logic and logic with fuzzy quantifiers (such as T-calculus and S-calculus and so on). The theorem proving system-TSetTAP is implemented by using SWI-PROLOG language in microcomputer. Using method of set signs in the system, rule programming can be generated by only increasing reasoning rules in rule base. System need not be repaired. So some strategies and approaches used in classical logics can easily apply to non-classical logic. 900 logic questions in TPTP are proved using the system. The result shows TSetTAP makes the tableau close early and improve greatly in time efficiency and space efficiency of reasoning.

关键词： Artificial intelligence

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A New Seamless Multi-resolution Simplification Method for the Terrain Model

A New Seamless Multi-resolution Simplification Method for th...

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Web Intelligence and Intelligent Agent Technology Workshops (WI-IAT)

作者： Huijie Zhang Jigui Sun Changsong Qi Nan Lv College of Computer Northeast Normal University Changchun China College of Computer Science & Technology Jilin University Changchun China College of Computer Science & Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University China

This paper presents a fast method to construct the simplified terrain model with the multi-resolution. In this method, we adopt the normal quad-tree hierarchy to subdivide the original terrain surface into the multi- resolution levels. For the sake of implementing the compressive storage and the efficient index for the elevation data, a connotative hierarchy is proposed and its corresponding index strategies are deduced. On the basis of the connotative hierarchy, we focus on resolving the crack problem of the simplified model. We firstly create the space filling curve during the simplification process to accelerate the search for the potential cracks. Afterwards, the different approaches are utilized to process the cracks in terms of the dissimilar terrain features. For this reason, we put forward an evaluation function that can selfadaptively identify the terrain feature according to the normal vector angle of the adjacent nodes in the connotative hierarchy. Consequently, there are not redundant triangles in our seamless multi-resolution terrain model. The proposed approaches are experimented on the real data and the results show that our method is relatively efficient and robust. Besides, the seamless simplified model has less number of triangles than the other common algorithm.

关键词： Multiresolution analysis Educational institutions Intelligent agent Computer science Surface cracks Filling Conferences Acceleration Robustness Sun

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Text Categorization Method Based on Improved Mutual Information and Characteristic Weights Evaluation Algorithms

Text Categorization Method Based on Improved Mutual Informat...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Zhili Pei Xiaohu Shi Maurizio Marchese Yanchun 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 Mathematics & Computer Science National University of Inner Mongolia Tongliao China Department of Information and Communication Technology University of Trento Povo TN USA

The improvement of text categorization by statistical methods can be performed from two main directions, namely the feature selection and the evaluation of characteristic weights. In this paper, we propose an enhanced text categorization method based on a modified mutual information algorithm and evaluation algorithm of characteristic weights which improves both aspects. The proposed method is applied to the benchmark test set Reuters-21578 Top10 to examine its effectiveness. Numerical results show that the precision, the recall and the value of F1 of the proposed method are all superior to those of existing conventional methods.

关键词： Text categorization Mutual information Frequency shift keying Frequency estimation Computer science Statistical analysis Testing Educational institutions Communications technology Performance evaluation

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Graph-based fast image segmentation

Graph-based fast image segmentation

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Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition, SSPR 2006 and SPR 2006

作者： Han, Dongfeng Li, Wenhui Lu, Xiaosuo Li, N. Wang, Yi Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun 130012 China

ISBN: (纸本)3540372369

In this paper, we describe a fast semi-automatic segmentation algorithm. A nodes aggregation method is proposed for improving the running time and a Graph-Cuts method is used to model the segmentation problem. The whole process is interactive. Once the users specify the interest regions by drawing a few lines, the segmentation process is reliably computed automatically no additional users' efforts are required. It is convenient and efficient in practical applications. Experiments are given and outputs are encouraging. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Image segmentation

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A novel method for mining sequential patterns in datasets

A novel method for mining sequential patterns in datasets

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ISDA 2006: Sixth International Conference on Intelligent Systems Design and Applications

作者： Xiaoyu, Chang Chunguang, Zhou Zhe, Wang Ping, Hu Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun 130012 China

ISBN: (纸本)0769525288

Sequential pattern mining is one of the most important fields in data mining. In this paper, we propose a novel algorithm FSPAN (Fast Sequential Pattern mining algorithm) to do the sequence mining. FSPAN can mine all the frequent sequential patterns in large datasets and it integrates a depth-first traversal approach with an effective pruning mechanism. This pruning mechanism solves the problem of searching frequent sequences in a sequence database by searching frequent items or frequent itemsets, which makes this method very efficient. Moreover, the databases scanned via FSPAN keep shrinking quickly, which makes the algorithm more efficient when the sequential patterns are longer. Experiments on standard test data show that FSPAN is very effective. © 2006 IEEE.

关键词： Data mining

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