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作者： Dong, Xuchu Yu, Haihong Ouyang, Dantong Ye, Yuxin Zhang, Yonggang Department of Computer Science and Technology Jilin University Changchun Jilin 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin 130012 China

ISBN: (纸本)9788988678251

For the optimization prob.em ab.ut triangulation of b.yesian networks, a novel genetic algorithm, DHGA, is proposed in this paper. DHGA employs a heuristic-b.sed mutation operation. Moreover, it uses population diversity to identify stagnation and convergence as well as to guide the search procedure. Experiments on representative b.nchmarks show that DHGA posses b.tter performance and rob.stness than other swarm intelligence methods.

关键词： b.yesian networks

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b.ock-b.sed normalized-cut algorithm for image segmentation

Block-based normalized-cut algorithm for image segmentation

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作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Chen, Jingrun Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Institute of Computer Graphics and Image Processing Dalian Nationalities University Dalian China State Key Laboratory of CAD and CG Zhejiang University Hangzhou China

ISBN: (纸本)9783642051722

Automatic image segmentation remains a challenging prob.em in the fields of computer vision, image analysis and understanding. A lot of algorithms and technologies have b.en proposed and developed for image segmentation, among which the N-cut has b.en a promising method and active research area due to b.tter quality. b.t the N-cut can't process medium and large image dataset online due to incredib.e memory and time complexity. Moreover, the N-cut algorithm can't utilize texture information since its b.sic unit of clustering algorithm is pixel. To overcome the drawb.ck and improve the description power, the paper proposes a b.ock-b.sed N-cut algorithm for image segmentation, whose b.sic unit of clustering is image b.ock instead of individual pixel. b.ock-b.sed algorithm not only decreases the time and storage complexity, b.t also improves the discrimination power of visual feature vector. The experiments demonstrate that proposed algorithm outperform the original N-cut algorithm in efficiency. Experiments show that the proposed b.ock-b.sed N-cut algorithm is more suitab.e for online processing large-scale data. © 2010 Springer-Verlag b.rlin Heidelb.rg.

关键词： Image segmentation

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Genetic algorithm with affinity propagation

Genetic algorithm with affinity propagation

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2010 IEEE International Conference on Information and Automation, ICIA 2010

作者： Wu, Chunguo Gao, Hao Yu, Lianjiang Liang, Yanchun Xiang, Rongwu College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry Jilin University Changchun 130012 China School of Basic Courses Shenyang Pharmaceutical University Shenyang 110016 China

ISBN: (纸本)9781424457021

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization prob.ems, e.g., aerodynamic design optimization, qualitative model learning in b.oinformatics. To address this prob.em, we present a comb.nation b.tween genetic algorithms and clustering methods. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known b.nchmark prob.ems in the term of optimization accuracy. ©2010 IEEE.

关键词： Genetic algorithms

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A hyb.id algorithm of minimum spanning tree and nearest neighb.r for classifying human cancers

A hybrid algorithm of minimum spanning tree and nearest neig...

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2010 3rd International Conference on Advanced Computer Theory and engineering, ICACTE 2010

作者： Zhou, Chunb.o Wan, Liming Liang, Yanchun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China Research Institute on General Development and Evaluation of Equipment EAAF of PLA Beijing 100076 China

ISBN: (纸本)9781424465408

Classification and prediction of different cancers b.sed on gene expression profiles are important for cancer diagnosis, cancer treatment and medication discovery. The k nearest neighb.r algorithm (k-NN) is one easy and efficient machine learning method for cancer classification and the parameter k is crucial. In this paper, we integrate minimum spanning tree (MST) and k nearest neighb.r algorithm (k-NN) for cancer classification. The MST is designed for the selection of parameter k and the nearest neighb.rs for k-NN. Firstly we b.ild a minimum spanning tree (MST) b.sed on Euclidean distance b.tween each two samples for gene expression data only including one unknown class sample. Secondly for unknown class sample in the gene expression data, we find the connected samples and then apply majority vote principle. Thirdly if there are tied votes then we expend the connected samples with the nearest neighb.rs for unknown class sample until all the samples are expended or the class for unknown sample is ob.ained. This hyb.id algorithm is referred to as MSTNN. The hyb.id algorithm MSTNN is compared with kNN and other 3 existing classification algorithms on CNS dataset, Colon dataset and Lung dataset, 3 b.nary class gene expression datasets and 3 multi-class gene expression datasets: Leukemial, Leukemia2, and Leukemia3 *** human cancers. The MSTNN algorithm improves 5.65% b.tter than k-NN on average LOOCV accuracy and 13.80% b.tter than kNN on testing datasets classification average accuracy, and achieves the b.st performance in all the 5 algorithms. The results demonstrate that the proposed MSTNN algorithm is feasib.e to classify human cancers. © 2010 IEEE.

关键词： Classification (of information)

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An adaptive multi-heuristic ant colony system for finding optimal elimination orderings in b.yesian networks

An adaptive multi-heuristic ant colony system for finding op...

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International Conference on Electrical engineering Computing Science and Automatic Control

作者： Dong, Xuchu Zhang, Yonggang Cai, Dianb. Yu, Haihong Ye, Yuxin Department of Computer Science and Technology Jilin University China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education China Network Monitor Maintenance Centre of Jilin Branch China Telecom Corporation Limited Changchun Jilin China

ISBN: (纸本)9781424473120

To find an optimal elimination ordering for b.yesian networks, a multi-heuristic-b.sed ant colony system named MHC-HS-ACS is proposed. MHC-HS-ACS uses a set of heuristics to guide the ants to search solutions. The heuristic set can evolve with the searching procedure in an adaptive way. MHC-HS-ACS also utilizes a heuristic-b.sed local search to accelerate its convergence. computational experiments show that MHC-HS-ACS can find very high quality solutions. © 2010 IEEE.

关键词： b.yesian networks

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A community mining algorithm for web.texts b.sed on Multi-agent system

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Journal of computational Information Systems 2010年第11期6卷 3509-3516页

作者： Yang, Hui Wang, Zhe Zhou, Xu Zhou, Tong Wang, Zuo College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory for Symbol Computation and Knowledge Engineering Ministry of Education China

Most of the existing methods for community discovery only deal with social network with a fixed structure, so they can not effectively deal with dynamic social network. This paper proposes a Multi-agent system method which is applied to real-time dynamic web.texts for community discovery. This method comb.nes the multi-agent system control mechanisms with community discovery algorithm. The approach not only divides community for the pub.ished web.text, b.t also changes the community division according to the newly updated contents of web.texts. Meanwhile, the similarity of two web.texts is evaluated through similarity algorithm which compares content similarity and semantic similarity. The effectiveness of this algorithm has b.en tested on real web.texts networks and experiment 3 illustrated the scalab.lity of our algorithm for dynamic community discovery of web.texts. © 2010 b.nary Information Press.

关键词： Semantics

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Optimization strategy of parallel query processing b.sed on multi-core architecture

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Journal of Convergence Information Technology 2010年第8期5卷 24-24页

作者： Chen, Yongheng Zuo, Wanli He, Fengling Chen, Kerui College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

Chip Multi-Processor (CMP) could support more than two threads to execute simultaneously, and some executing units are owned b. each core. b.cause threads share various resources of CMP, such as L2-Cache, among many threads, CMP system is inherently different from multiprocessors system and, CMP is also different from simultaneously multithreading (SMT). In this paper a novel and complete approach on how to parallelism for relational datab.se multithreaded query execution that strives for maximum resource utilization for b.th CPU and disk activities. The focus of this approach is on how to use the multithreaded parallel technique to optimize and process queries b.sed on multi-core architecture. A set of algorithms for implementing and optimizing the b.st query plan, such as the algorithms for scheduling and parallel executing the query plan, the algorithms for allocating thread to sub.query and memory to the b.ffers b.tween operations in pipelining execution, are proposed in this method. Additionally this paper analysis and optimize the implementation of parallel b.ffers and multithread. In the experiments, this paper evaluates performance of the parallel b.ffers and tests the coordination of the multithread.

关键词： Scheduling algorithms

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A hyb.id discrete PSO-SA algorithm to find optimal elimination orderings for b.yesian networks

A hybrid discrete PSO-SA algorithm to find optimal eliminati...

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2010 2nd International Conference on Industrial and Information Systems, IIS 2010

作者： Dong, Xuchu Ouyang, Dantong Cai, Dianb. Zhang, Yonggang Ye, Yuxin Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun Jilin China Network Monitor Maintenance Centre Jilin Branch China Telecom Corporation Limited Changchun Jilin China

ISBN: (纸本)9781424482177

In this paper, a hyb.id algorithm named DPSO-SA is proposed to find near-to-optimal elimination orderings in b.yesian networks. DPSO-SA is a discrete particle swarm optimization method enhanced b. simulated annealing. computational tests show that this hyb.id method is very effective and rob.st for the elimination ordering prob.em. © 2010 IEEE.

关键词： Simulated annealing

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Cooperative coevolutionary genetic algorithms to find optimal elimination orderings for b.yesian networks

Cooperative coevolutionary genetic algorithms to find optima...

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2010 IEEE 5th International Conference on b.o-Inspired Computing: Theories and Applications, b.C-TA 2010

作者： Dong, Xuchu Yu, HaiHong Ouyang, Dantong Cai, Dianb. Ye, Yuxin Zhang, Yonggang Department of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun Jilin China Network Monitor Maintenance Centre Jilin Branch China Telecom Corporation Limited Changchun Jilin China

ISBN: (纸本)9781424464388

According to the characteristics of the optimal elimination ordering prob.em in b.yesian networks, a heuristic-b.sed genetic algorithm, a cooperative coevolutionary genetic framework and five grouping schemes are proposed. b.sed on these works, six cooperative coevolutionary genetic algorithms are constructed. Numerical experiments show that these algorithms are more rob.st than other existing swarm intelligence methods when solving the elimination ordering prob.em. © 2010 IEEE.

关键词： b.yesian networks

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Analysis the uncertainties of the Ant Colony Algorithm

Analysis the uncertainties of the Ant Colony Algorithm

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International Conference on computational Aspects of Social Networks, CASON

作者： Zhang, Yi Zhang, Meng Cao, Xin-Yan Department of Information Engineer Jilin Business and Technology College Changchun 130062 China College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun 130012 China College of Electronic Information Engineering University of Changchun Changchun Jilin Province 130000 China

ISBN: (纸本)9780769542027

In this paper, the sub.ect of discussion is the uncertainties of Ant Colony Algorithm(ACA). In order to find application and popularize the ACA, we try to find some disciplinarians which can eliminate the impact of uncertainties in our algorithm and find the b.st assemb.ed of the parameters b. a series of experiments. Results show that our works had great effects in practicality. © 2010 IEEE.

关键词： Traveling salesman prob.em

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