In this paper, we investigate one-class and clustering problems by using statistical learning theory. To establish a universal framework, a unsupervised learning problem with predefined threshold η is formally descri...
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Resource Space Model (RSM) is a semantic model to manage and share heterogeneous resources on the Internet. This paper focuses on the general architecture, physical implementation and application of RSM. The RSM syste...
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In the current Web, e-document has been the most common vehicle for delivering and exchanging information. As the amount of e-documents has grown enormously, effective classification facilities are urgently needed to ...
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The Semantic Link Network model SLN and Resource Space Model RSM are semantic models proposed separately for effectively specifying and managing versatile resources across the Internet. Collaborating the relational se...
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The Knowledge Grid is an intelligent and sustainable Internet application environment that enables people and roles to effectively capture, publish, share and manage explicit knowledge resources. As an important funct...
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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 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.
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
Based on the quotient space granular theorem, the image segmentation concept is analyzed and the image segmentation methods are studied, and then the quotient space granular theorem of image segmentation is demonstrat...
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Based on the quotient space granular theorem, the image segmentation concept is analyzed and the image segmentation methods are studied, and then the quotient space granular theorem of image segmentation is demonstrated. The image segmentation problems are described with triple elements function of the quotient space model (X,f,Γ)⇔([X],[f],[Γ]), according to the quotient space granularity computing, the image segmentation theorem is presented. The methods of images segmentation based on hierarchical and synthesis and combinational technique are exploited and then the segmentation algorithm based granularity synthesis technique is proposed. In this technique, the features of directionality and roughness in texture images data set are firstly extracted respectively to form the different granularities of image, then the different granularity are synthesized according to the theorem of granularity synthesis, finally the texture images is segmented. The experimental results demonstrate that the algorithm is valid for the segmentation of complicated texture images.
Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose...
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Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose a new concept of probability information distance (PID) and prove that the PID satisfies four requests of axiomatization of the distance. So the PID is a kind of distance measure, which can be used to measure the degree of variation between two random variables. We make the PID be separability criterion of the classes for information feature extraction, and call it PID criterion (PIDC). Based on PIDC, we design a novel algorithm for information feature extraction. Compared with principal components analysis (PCA), correlation analysis etc., the algorithm put forward in this paper had regarded for the class information, and so it is a kind of supervised algorithm of feature extraction. The experimental results demonstrate that the algorithm is valid and reliable, and it provides a new research approach for feature extraction, data mining and pattern recognition.
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