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Your search keyword '"Xue Xiao"' showing total 29 results
Start Over Author "Xue Xiao" Publication Type Books
29 results on '"Xue Xiao"'

16. yao yong zhi wu xue 药用植物学

Author

quan guo zhong deng wei sheng xue xiao shi yong jiao cai yao yong zhi wu xue bian xie zu zhu bian 全国中等卫生学校 试用教材药用植物学 编写组主编, Institute of Botany, Chinese Academy of Sciences, and quan guo zhong deng wei sheng xue xiao shi yong jiao cai yao yong zhi wu xue bian xie zu zhu bian 全国中等卫生学校 试用教材药用植物学 编写组主编

Subjects
Botany
Published
1979

17. chu zhong zhi wu xue wen ti jie da

Author

he nan shi fan zhuan ke xue xiao fu she jiao xue ye wu tong xun zhan bian, Internet Archive (archive.org), and he nan shi fan zhuan ke xue xiao fu she jiao xue ye wu tong xun zhan bian

Subjects
Botany
Published
1956

28. Towards a Customized Methodology to Develop Multi-Agent Systems.

Author

Shi, Zhong-Zhi, Sadananda, Ramakoti, Xue, Xiao, Liu, Xingquan, and Li, Rong

Abstract

Despite a great deal of research, there still exists a number of challenges before making agent-based computing a widely accepted paradigm in software engineering practice. In order to realize an engineering change in agent oriented software engineering, it's necessary to turn agent oriented software abstractions into practical tools for facing the complexity of modern application areas. The paper presents a customizable development architecture for multi-agent systems, which can empower the developer to assemble a methodology tailored to the given project by putting appropriate meta models together, much like developers building applications from third party off-the-shelf components. To exemplify its feasibility and effectivity, the construction of C4I system is presented as a case study. [ABSTRACT FROM AUTHOR]

Published
2006
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29. Locating Regions of Interest in CBIR with Multi-instance Learning Techniques.

Author

Zhang, Shichao, Jarvis, Ray, Zhou, Zhi-Hua, Xue, Xiao-Bing, and Jiang, Yuan

Abstract

In content-based image retrieval (CBIR), the user usually poses several labelled images and then the system attempts to retrieve all the images relevant to the target concept defined by these labelled images. It may be helpful if the system can return relevant images where the regions of interest (ROI) are explicitly located. In this paper, this task is accomplished with the help of multi-instance learning techniques. In detail, this paper proposes the CkNN-ROI algorithm, which regards each image as a bag comprising many instances and picks from positive bag the instance that has great chance to meet the target concept to help locate ROI. Experiments show that the proposed algorithm can efficiently locate ROI in CBIR process. [ABSTRACT FROM AUTHOR]

Published
2005
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