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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Dong, Xuchu Yu, HaiHong Ouyang, Dantong Cai, Dianbo 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 problem in Bayesian networks, a heuristic-based genetic algorithm, a cooperative coevolutionary genetic framework and five grouping schemes are proposed. Based on these works, six cooperative coevolutionary genetic algorithms are constructed. Numerical experiments show that these algorithms are more robust than other existing swarm intelligence methods when solving the elimination ordering problem. © 2010 IEEE.

关键词： Bayesian networks

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A hybrid discrete PSO-SA algorithm to find optimal elimination orderings for Bayesian 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, Dianbo 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 hybrid algorithm named DPSO-SA is proposed to find near-to-optimal elimination orderings in Bayesian networks. DPSO-SA is a discrete particle swarm optimization method enhanced by simulated annealing. computational tests show that this hybrid method is very effective and robust for the elimination ordering problem. © 2010 IEEE.

关键词： Simulated annealing

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Soil fertility grading with Bayesian Network transfer learning

Soil fertility grading with Bayesian Network transfer learni...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hai-Yang Jia Juan Chen He-Long Yu Da-You Liu College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China College of Information technology Jilin agricultural University Changchun Jilin China

Soil fertility grading is an important issue in the agriculture domain, AI based approach has been applied recently. But in most circumstance data obtaining is a expensive and time consuming procedure, sometimes even impossible. This paper presents a Bayesian Network based transfer learning algorithm. The existing training results can be transferred between the nearby land squares. The proposed algorithm considers both the similarity between the learning task and the geographical position of the land squares. Empirical experiment were implemented to prove the efficiency of the algorithm.

关键词： Bayesian methods Machine learning Learning Soil Random variables Machine learning algorithms Training data

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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 subject 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 best assembled of the parameters by a series of experiments. Results show that our works had great effects in practicality. © 2010 IEEE.

关键词： Traveling salesman problem

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Automatic generation of domain-specific ontology from deep web

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Journal of Information and computational Science 2010年第2期7卷 519-525页

作者： Chen, Kerui Zuo, Wanli Zhang, Fan He, Fengling Peng, Tao College of Computer Science and Technology Jilin University Changchun 130012 China College of Information Engineering Zhongzhou University Zhengzhou 450044 China Key Laboratory of Computation and Knowledge Engineering Ministry of Education China

In Deep Web, there are various methods of studying extraction of data records. After carefully reviewing and analyzing those methods, we find that the problem of dealing with either zero or fewer query results and nested data structure still exists. By using ontology's cooperation, the problem would be solved easily. We observe that query interfaces and data pages of the sources often contain rich information on concepts, instances, and concept relationships in the application domain. This proposal proposes a method combined query interface and query result, to automatically extract specific domain ontology. In addition, the ontology model, which was designed based on the structured and semi-structured characteristics of deep web, also can assist the process of extracting deep web result pages. Experimental results approve that our method can get more comprehensive concepts, simple domain-specific ontology is more convenient for being applied in data extraction. Copyright © 2010 Binary Information Press.

关键词： Ontology

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Semantic Retrieval System Based on Corn Ontology

Semantic Retrieval System Based on Corn Ontology

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Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST)

作者： Hong Qi Liangliang Zhang Ying Gao Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Computer Science and Technology Jilin University China Alcatel Lucent Shanghai Bell Company Limited China Jilin University Changchun Jilin CN

Most of the current information retrieval systems are mainly based on full text matching of keywords or topic-based classification, often return a large number of irrelevant information, and are unable to meet the user's request. Ontology-based semantic retrieval is a hot issue in current research. In this paper, the corn plant ontology is constructed using Formal Concept Analysis based approach in which the concept lattice is built from terminology-file relationship table and further reduced. Based on the corn plant ontology, we propose a semantic annotation method in which the feature words are selected by an improved method for weight calculation and the RDF triples are generated by syntactic parser. Finally a semantic retrieval system for corn plant is developed. In comparative experiment one hundred documents are selected as the dataset, and the result shows that the semantic retrieval system introduced in this paper is superior to keyword-based retrieval method in precision ratio and recall ratio.

关键词： Semantics Ontologies Information retrieval Lattices Feature extraction Resource description framework Optical wavelength conversion

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A Stable Stochastic Optimization Algorithm for Triangulation of Bayesian Networks

A Stable Stochastic Optimization Algorithm for Triangulation...

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International Workshop on knowledge Discovery and Data Mining (WKDD)

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

ISBN: (纸本)9781424453979

In this paper, we present a novel deterministic heuristic and a new genetic algorithm to solve the problem of optimal triangulation of Bayesian networks. The heuristic, named MinFillWeight, aims to select variables minimizing the multiplication of the weights on nodes of fill-in edges. The genetic algorithm, named GA-MFW, uses a new rank-reserving crossover operator and a 2-fold mutation mechanism utilizing the MinFillWeight heuristic. Experiments on representative benchmark show that the deterministic heuristic and the stochastic algorithm have good performance and stability to various problems.

关键词： Stochastic processes Bayesian methods Tree graphs Ethics Genetic algorithms Inference algorithms Genetic mutations Stability Ant colony optimization Data mining

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A Multi-heuristic Cooperative Ant Colony System for Optimizing Elimination Ordering of Bayesian Networks

A Multi-heuristic Cooperative Ant Colony System for Optimizi...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

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

To solve the problem of searching for an optimal elimination ordering of Bayesian networks, a novel effective heuristic, MinSum Weight, and an ACS approach incorporated with multi-heuristic mechanism are proposed. The ACS approach named MHC-ACS utilizes a set of heuristics to direct the ants moving in the search space. The cooperation of multiple heuristics helps ants explore more regions. Moreover, the most appropriate heuristic will be identified and be reinforced with the evolution of the whole system. Experiments demonstrate that MHC-ACS has a better performance than other swarm intelligence methods.

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Ontology-assisted schema matching for deep web query interfaces

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Journal of Information and computational Science 2010年第2期7卷 543-549页

作者： Wang, Ying Zuo, Wanli Wang, Xin Zhang, Aiqi Peng, Tao College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Computation and Knowledge Engineering Ministry of Education China College of Software Changchun Institute of Technology Changchun 130012 China

Schema matching is important for schema integration and thus has got great attention. In this paper, we present an ontology-based algorithm to match a larger number of interface schemas, which can hand both simple and complex mappings of attributes. Firstly, construct a core ontology and the semantics of concepts in the core ontology can be enriched during schema matching. Secondly, design the ontology-assisted schema matching algorithmic for deep web query interface. Lastly, capture the matching relationships between source schema and target schema using domain-specific ontology. During the schema matching of deep web entries, ontology is the ″bridging″ which is used to obtain the mapping relationships between different query interfaces. Experiment result shows that this method is feasible and effective. Copyright © 2010 Binary Information Press.

关键词： Ontology

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Adaptive feature selection and extraction approaches for image retrieval based on region

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Journal of Multimedia 2010年第1期5卷 85-92页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Key Laboratory of Symbol Computation and Knowledge Engineering of 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

Image retrieval based on region is one of the most promising and active research directions in recent year's CBIR, while region segmentation, feature selection and feature extraction of region are key issues. However, the existing approaches always adopt a uniform approach of segmentation and feature extraction for all images in the same system. In this paper, we propose adaptive image segmentation and feature extraction approach according to different category image for image retrieval system. To improve performance, we propose adaptive segmentation approach according to different category image. Textured image is segmented by Gaussian Mixture Models (GMM), while non-textured image is segmented by our proposed block-based normalized cut. To accurately describe feature of region, we propose weight assignment method for centroid pixel and its neighbor by convolution with normal distribution when image segmentation by GMM. To improve generalization, we propose adaptive number of Fourier descriptors of shape signature which depends on the energy distribution of Fourier descriptors, instead of fixed number by experience. To simply and efficiently describe the spatial relationships of multi-object or multi-region in same image, we apply simplified topological relationships. The experiments demonstrate that proposed approaches are superior to the traditional approaches. © 2010 ACADEMY PUBLISHER.

关键词： Feature extraction

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