• Korean Journal of Mathematics
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• v.25 no.4
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• pp.579-585
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• 2017

Based on injectivity, we introduce some definitions in the category NFHG of normal fuzzy hypergroups. And we show that a complete normal fuzzy hypergroup is a weakly injective object in NFHG. Also we investigate weak injectivity in the comma category NFHG/K.

• Language and Information
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• v.14 no.2
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• pp.71-84
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• 2010

The aim of this paper is to propose that a category P is encapsulated within a functional layer above the lexical layer, just like vP containing a lexical VP. As is well known, the category P has long been in the obscure domain of syntactic studies: Marantz (2001) and den Dikken (2003), for example, argue that P is a lexical category, but Emonds (1985), Grimshaw (1991), and Baker (2003), maintain that the category P is functional and is a closed category without its own intrinsic meaning. On the other hand, Zwart (2005) argues that it does have some meaning. Following the works of Svenonius (2003, 2006, 2007), and the spirit of Rizzi's (1997) split CP hypothesis, we elaborate and develop Svenonius' idea of split-pP analysis with detailed schematic representations of the novel examples in English, Korean, and Chinese in this paper. Unlike Svenonius, however, this paper incorporates KP into pP-Shell, which is a substantial simplification. Furthermore, Chinese Localizers that have long been considered as Postpositions are now under the category of Prepositions. This proposal renders an X-bar theoretic consistency over the categorical status of Chinese phrasal structures. In short, the present analysis accounts for inconsistency found in English complex preposition phrase (Quirk, et al, 1972, 1985), Chinese circumposition phrase (Ernst 1988, Liu, 2002) and Korean postposition phrase in a unified and consistent manner. Furthermore, by proposing a finer-grained phrasal architecture for the category P, the controversial status of the category subsides within this analysis.

• Journal of the Korean Mathematical Society
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• v.59 no.1
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• pp.171-192
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• 2022

For any category with a distinguished collection of sequences, such as n-exangulated category, category of N-complexes and category of precomplexes, we consider its Grothendieck group and similar results of Bergh-Thaule for n-angulated categories [1] are proven. A classification result of dense complete subcategories is given and we give a formal definition of K-groups for these categories following Grayson's algebraic approach of K-theory for exact categories [4].

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.19-29
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• 2013

In the current Internet environment where there is high space complexity of information, search engines aim to provide accurate information that users want. But content-based method adopted by most of search engines cannot be used as an effective tool in the current Internet environment. As content-based method gives different weights to each web page using morphological characteristics of vocabulary, the method has its drawbacks of not being effective in distinguishing each web page. To resolve this problem and provide useful information to the users, this paper proposes an evaluation method based on categories. Category-based evaluation method is to extend query to semantic relations and measure the similarity to web pages. In applying weighting to web pages, category-based evaluation method utilizes user response to web page retrieval and categories of query and thus better distinguish web pages. The method proposed in this paper has the advantage of being able to effectively provide the information users want through search engines and the utility of category-based evaluation technique has been confirmed through various experiments.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1417-1424
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• 2018

In order to improve the performance of image classifications using Convolutional Neural Networks (CNN), applying a category hierarchy to the classification can be a useful idea. However, the visual separation of object categories is very different according to the upper and lower category levels and highly uneven in image classifications. Therefore, it is doubtable whether the use of category hierarchies for classification is effective in CNN. In this paper, we have clarified whether the image classification using category hierarchies improves classification performance, and found at which level of hierarchy classification is more effective. For experiments we divided the image classification task according to the upper and lower category levels and assigned image data to each CNN model. We identified and compared the results of three classification models and analyzed them. Through the experiments, we could confirm that classification effectiveness was not improved by reduction of number of categories in a classification model. And we found that only with the re-training method in the last network layer, the performance of lower category classification was not improved although that of higher category classification was improved.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.965-978
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• 2006

This paper implements an automatic Korean word-spacing system based on word-recognition using morpheme unigrams and the pattern that the categories of those morpheme unigrams share within a candidate word. Although previous work on Korean word-spacing models has produced the advantages of easy construction and time efficiency, there still remain problems, such as data sparseness and critical memory size, which arise from the morpho-typological characteristics of Korean. In order to cope with both problems, our implementation uses the stochastic information of morpheme unigrams, and their category patterns, instead of word unigrams. A word's probability in a sentence is obtained based on morpheme probability and the weight for the morpheme's category within the category pattern of the candidate word. The category weights are trained so as to minimize the error means between the observed probabilities of words and those estimated by words' individual-morphemes' probabilities weighted according to their categories' powers in a given word's category pattern.

• Journal of the Korea Computer Industry Society
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• v.3 no.7
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• pp.881-890
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• 2002

In this paper, by using agent technology, we proposes and implements the search system that supplies the result close to the user preference through the analysis of user preference. To offer better qualified information to user without redundant search results and unnecessary information of legacy search system. this system uses user's information and generates keywords and categories. Comparing user's favorite category with search result of legacy search system through the agent oriented search engine, it supplies only the result close to the user's category. At the same time, search result is saved into the databases according to each category to be used for search work later. As a result, the redundant information of search result was efficiently removed and the information close to the user's favorite category was obtained.

• Journal of KIISE:Software and Applications
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• v.37 no.5
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• pp.378-385
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• 2010

The explosive increase in the use of email has made to need email classification efficiently and accurately. Current work on the email classification method have mainly been focused on a binary classification that filters out spam-mails. This methods are based on Support Vector Machines, Bayesian classifiers, rule-based classifiers. Such supervised methods, in the sense that the user is required to manually describe the rules and keyword list that is used to recognize the relevant email. Other unsupervised method using clustering techniques for the multi-category classification is created a category labels from a set of incoming messages. In this paper, we propose a new automatic email multi-category classification method using NMF for automatic category label construction method and dynamic category hierarchy method for the reorganization of email messages in the category labels. The proposed method in this paper, a large number of emails are managed efficiently by classifying multi-category email automatically, email messages in their category are reorganized for enhancing accuracy whenever users want to classify all their email messages.

• Journal of Korea Multimedia Society
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• v.9 no.10
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• pp.1332-1343
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• 2006

This paper proposes an educational community service system that manages information of good quality intensively without overlapping, and that provides an effective searching function by using a personal community with many user layers. This system raises the efficiency of searching and management by using Metadata and Category. It is a self-leading educational community system that brings merits into relief and improves weak points. We constructed an autogenous Blog chain service as a tool which verifies this system, which is called 'EduLOG(Educational Blog) service' Especially we extracted Metadata suitable for this service, which is on the basis of worldfamous Dublin Core Metadata. And we made a new category on the basis of categories which were proposed by some educational community sites and public educational authorities, and we applied it to this system. To ascertain whether this service system provide adequate function or not, we made a questionnaire on the basis of the appraisal table in websites, and evaluated it at the request of experts. In view of the results so far achieved, it returned good scores (above 3.5/5.0) in accuracy of evaluation, low-end reappearance ratio, easiness of registering and approaching information, and intensive management of a categorical information, confirming the efficiency of the ECSS system. Therefore we believe firmly that the ECSS system will play a efficient information storing and searching roles in the near future.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.79-89
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• 2004

Since the amount of E-mail messages has increased , we need a new technique for efficient e-mail classification. E-mail classifications are grouped into two classes: binary classification, multi-classification. The current binary classification methods are mostly spm mail classification methods which are based on rule driven, bayesian, SVM, etc. The current multi- classification methods are based on clustering which groups e-mails by similarity. In this paper, we propose a novel method for e-mail classification. It combines the automatic category generation method based on the vector model and the dynamic category hierarchy construction method. This method can multi-classify e-mail automatically and manage a large amount of e-mail efficiently. In addition, this method increases the search accuracy by dynamic reclassification of e-mails.