Based on Jordan curve theorem, a universal classification method based on hyper surface is recently put forward. The experiments show that the new method can efficiently and accurately classify large data size up to 1...
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Based on Jordan curve theorem, a universal classification method based on hyper surface is recently put forward. The experiments show that the new method can efficiently and accurately classify large data size up to 10/sup 7/ in three-dimensional space. However, the number of training samples needed to design a classifier grows with the dimension of the features. So a way to reduce the dimension of the features without losing any essential information is needed. We put forward a kind of simple and efficient dimension reduction method without losing any essential information to improve the performance of classification based on hyper surface for high dimension data.
In this paper, we present a brief summary to 3D mesh model segmentation techniques, including definition, latest achievements, classification and application in this field. Then evaluations on some of typical methods,...
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In this paper, we present a brief summary to 3D mesh model segmentation techniques, including definition, latest achievements, classification and application in this field. Then evaluations on some of typical methods, such as Watershed, topological and geometrical method, are introduced. After some applications are presented, problems and prospect of the techniques are also discussed.
This paper presents a novel segmentation method based on a non-parametric background model that has the ability of modeling multi-model. Firstly, both the intensity and edge features are used to improve robustness of ...
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This paper presents a novel segmentation method based on a non-parametric background model that has the ability of modeling multi-model. Firstly, both the intensity and edge features are used to improve robustness of the foreground detection. Secondly, we also present an adaptive shadow detection model to find the accurate moving objects. The experiment results show that our proposed method is effective.
The translation of inheritance nets to default logic has been discussed by Etherington[9],Touretzky[10],*** and inheritance nets are similar in some aspects and based on methods of translating inheritance nets to defa...
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The translation of inheritance nets to default logic has been discussed by Etherington[9],Touretzky[10],*** and inheritance nets are similar in some aspects and based on methods of translating inheritance nets to default logic,a translation of ontologies to default logic with a priority order on defaults is ***,properties of an ontology and the revision of ontologies can be studied in terms of default *** are assumed to be trees under the subsumption relation between concepts and have deduction rules to infer what are not explicitly *** statements in ontologies are translated to facts of default theories of the ontologies and the default inheritance of properties are represented by normal defaults with a priority order on them due to the intuition that subclasses overriding *** an ontology with a tree structure,it is consistent if and only if the default theory of the ontology has a unique extension.
This paper proposes a hierarchical iterative and self-supervised method (HISS) to acquire concept words from a large-scale, un-segmented Chinese corpus. It has two levels of iteration: the EM-CLS algorithm and the Vit...
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This paper proposes a hierarchical iterative and self-supervised method (HISS) to acquire concept words from a large-scale, un-segmented Chinese corpus. It has two levels of iteration: the EM-CLS algorithm and the Viterbi-C/S algorithm constitute the inner iteration for generating concept words, and the concept word validation constitutes the outer iteration together with the concept word generation. Through multiple iterations, it integrates the concept word generation and validation into a uniform acquisition process. In the process of acquisition, the HISS method can cope with the problem of over-segmentation, over-combination and data sparseness. The experimental result shows that the HISS method is valid for concept word acquisition that can simultaneously increase the precision and recall rate of concept word acquisition.
作者：
Hai ZhugeChina Knowledge Grid Research Group
Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China
In the human, society, interconnection environment and systems methodology perspectives, this paper answers the following questions: What are the Knowledge Grid and its distinguished features? What are its methodology...
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In the human, society, interconnection environment and systems methodology perspectives, this paper answers the following questions: What are the Knowledge Grid and its distinguished features? What are its methodology and major research issues? These answers are important to the development of this promising area.
Based on fuzzy association degree, a new pattern recognition algorithm is set up. First, some new concepts of fuzzy association coefficient (FAC), fuzzy association degree (FAD) and fuzzy relative weight (FRW) have be...
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Based on fuzzy association degree, a new pattern recognition algorithm is set up. First, some new concepts of fuzzy association coefficient (FAC), fuzzy association degree (FAD) and fuzzy relative weight (FRW) have been proposed for surveying data information. Second, on the basis of the concepts proposed here, a new pattern recognition algorithm has been set up. At last, the algorithm set up here is applied to surveying data. The results of simulation application show that the recognition algorithm presented here is feasible and effective
WWW is a repository of information mainly oriented to human consumption. The lack of explicit and formal expression of data semantics makes the Web increasingly difficult to use and exploit. To remedy it, knowledge ma...
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WWW is a repository of information mainly oriented to human consumption. The lack of explicit and formal expression of data semantics makes the Web increasingly difficult to use and exploit. To remedy it, knowledge management sphere (KMSphere), taking advantages of semantic Web, Web services and virtual organizations, is proposed to explore important aspects of service-oriented and ontology-driven knowledge management on the grid. By building two kinds of mappings, ontologies construct a knowledge space on top of data repositories. KMSphere, based on OGSA, emphasizes how to organize, discover, utilize, and manage the knowledge resources in that space. This paper describes in detail the architecture of KMSphere.
The positive region in rough set framework and Shannon conditional entropy are two traditional uncertainty measurements, used usually as heuristic metrics in attribute reduction. In this paper first a new information ...
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The positive region in rough set framework and Shannon conditional entropy are two traditional uncertainty measurements, used usually as heuristic metrics in attribute reduction. In this paper first a new information entropy is systematically compared with Shannon entropy, which shows its competence of another new uncertainty measurement. Then given a decision system we theoretically analyze the variance of these three metrics under two reverse circumstances, Those are when condition (decision) granularities merge while decision (condition) granularities remain unchanged. The conditions that keep these measurements unchanged in the above different situations are also figured out. These results help us to give a new information view of attribute reduction and propose more clear understanding of the quantitative relations between these different views, defined by the above three uncertainty measurements. It shows that the requirement of reducing a condition attribute in new information view is more rigorous than the ones in the latter two views and these three views are equivalent in a consistent decision system.
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