• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.241-248
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• 2002

With the structure of tilde-area computing system which Is specified by a researching team in Vrije University, Netherlands, lots of researchers and developers have been progressing the studies of global location and interconnection services of distributed objects existing in global sites. Most of them halve focused on binding services of only non-duplicated computational objects existing wide-area computing sites without any consideration of duplication problems. But all of objects existing on the earth rave the duplicated characteristics according to how to categorize their own names or properties. These objects with the same property can define as duplicated computational objects. Up to now, the existing naming or trading mechanism has not supported the binding services of duplicated objects, because of deficiency of independent location service. For this reason, we suggest a new model that can not only manages locations of duplicated objects In wide-area computing environments, but also provide minimum binding time by considering both the optimal selection of one of duplicated objects and load balance among distributed systems. Our model is functionally divided into 2 parts, one part to obtain an unique object handle of duplicated objects with same property as a naming and trading service, and the other to search one or more contact addresses by a node manager using a liven object handle, as a location service For location transparency, these services are independently executing each other. Based on our model, we described structure of wide-area integrated tree and algorithms for searching and updating contact address of distributed object on this tree. finally, we showed a federation structure that can globally bind distributed objects located on different regions from an arbitrary client object.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.457-464
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• 2008

In the real-world operational environment, most of text classification systems have the problems of insufficient training documents and no prior knowledge of feature space. In this regard, $Na{\ddot{i}ve$ Bayes is known to be an appropriate algorithm of operational text classification since the classification model can be evolved easily by incrementally updating its pre-learned classification model and feature space. This paper proposes the improving technique of $Na{\ddot{i}ve$ Bayes classifier through feature weighting strategy. The basic idea is that parameter estimation of $Na{\ddot{i}ve$ Bayes considers the degree of feature importance as well as feature distribution. We can develop a more accurate classification model by incorporating feature weights into Naive Bayes learning algorithm, not performing a learning process with a reduced feature set. In addition, we have extended a conventional feature update algorithm for incremental feature weighting in a dynamic operational environment. To evaluate the proposed method, we perform the experiments using the various document collections, and show that the traditional $Na{\ddot{i}ve$ Bayes classifier can be significantly improved by the proposed technique.