• Title/Summary/Keyword: Classification structure

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A Study on Robust Emotion Classification Structure Between Heterogeneous Speech Databases (이종 음성 DB 환경에 강인한 감성 분류 체계에 대한 연구)

  • Yoon, Won-Jung;Park, Kyu-Sik
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.5
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    • pp.477-482
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    • 2009
  • The emotion recognition system in commercial environments such as call-center undergoes severe system performance degradation and instability due to the speech characteristic differences between the system training database and the input speech of unspecified customers. In order to alleviate these problems, this paper extends traditional method of emotion recognition of neutral/anger into two-step hierarchical structure by using emotional characteristic changes and differences of male and female. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance about 25% over the traditional method of emotion recognition.

A Study on Intellectual Structure of Library and Information Science in Korea (문헌정보학의 지식 구조에 관한 연구)

  • Yoo, Yeong-Jun
    • Journal of the Korean Society for information Management
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    • v.20 no.3
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    • pp.277-297
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    • 2003
  • This study was conducted upon the premise that index terms display the intellectual structure of a specific subject field. In this study, and attempt was made to grasp the intellectual structure of Library and Information. Science by clustering the index terms of the journals of the related academic societies at the Library of National Assembly - such as the Journal of the Korean Society for Information Management, the Journal of the Korean Library and Information Science Society, and the Journal of the Korean Society for Library and Information Science. Through the course of the study, index term clusters were generated based on the linkage of the index terms and the frequency of co-occurrence, and moreover, time periods analysis was conducted along with studies on first-appearing terms, in order to clarify the trend and development process of the Library and Information Science. This study also analysed the difference between two intellectual structure by comparing the structure generated by index term clusters with the existing structure of traditional classification systems.

Forest Vertical Structure Classification in Gongju City, Korea from Optic and RADAR Satellite Images Using Artificial Neural Network (광학 및 레이더 위성영상으로부터 인공신경망을 이용한 공주시 산림의 층위구조 분류)

  • Lee, Yong-Suk;Baek, Won-Kyung;Jung, Hyung-Sup
    • Korean Journal of Remote Sensing
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    • v.35 no.3
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    • pp.447-455
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    • 2019
  • Since the forest type map in Korea has been mostly constructed every five years, the forest information from the map lacks up-to-date information. Forest research has been carried out by aerial photogrammetry and field surveys, and hence it took a lot of times and money. The vertical structure of forests is an important factor in evaluating forest diversity and environment. The vertical structure is essential information, but the observation of the vertical structure is not easy because the vertical structure indicates the internal structure of forests. In this study, the index map and texture map produced from KOMPSAT-3/3A/5 satellite images and the canopy information generated by the difference between DSM (Digital Surface Model) and DTM (Digital Terrain Model) were classified using the artificial neural network. The vertical structure of forests of single and multi-layer forests was classified to identify 81.59% of the final classification result.

A Study on Building Internal Tables in Christianity of the 5th Edition of Korean Decimal Classification (기독교 분야 내부보조표 설정에 관한 연구 - 한국십진분류법 제5판을 중심으로 -)

  • Jeong, Yu Na;Chung, Yeon-Kyoung
    • Journal of the Korean BIBLIA Society for library and Information Science
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    • v.24 no.3
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    • pp.29-51
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    • 2013
  • The purpose of this study is to develop internal tables in Christian religion in the 5th edition of Korean Decimal Classification. The scope of the Christianity, its structure of various classification schemes, and the concepts of internal tables were analyzed. The contents of several textbooks were analyzed for the scope of the discipline and the classification schemes and internal tables of DDC, UDC, NDC, LCC, Classification of the Library of Union Theological Seminary and the Classification of the Korea Theological Library were compared. And then, internal tables in Bible, sermon, worship, church history were built and those tables were evaluated by librarians and experts in the fields. And finally, internal tables of the Christiainity and new headings were suggested. New internal tables in Christianity will increase the effectiveness of information retrieval and it will provide a foundation for developing internal tables in other disciplines.

Improving Classification Accuracy in Hierarchical Trees via Greedy Node Expansion

  • Byungjin Lim;Jong Wook Kim
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.6
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    • pp.113-120
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    • 2024
  • With the advancement of information and communication technology, we can easily generate various forms of data in our daily lives. To efficiently manage such a large amount of data, systematic classification into categories is essential. For effective search and navigation, data is organized into a tree-like hierarchical structure known as a category tree, which is commonly seen in news websites and Wikipedia. As a result, various techniques have been proposed to classify large volumes of documents into the terminal nodes of category trees. However, document classification methods using category trees face a problem: as the height of the tree increases, the number of terminal nodes multiplies exponentially, which increases the probability of misclassification and ultimately leads to a reduction in classification accuracy. Therefore, in this paper, we propose a new node expansion-based classification algorithm that satisfies the classification accuracy required by the application, while enabling detailed categorization. The proposed method uses a greedy approach to prioritize the expansion of nodes with high classification accuracy, thereby maximizing the overall classification accuracy of the category tree. Experimental results on real data show that the proposed technique provides improved performance over naive methods.

Segmentation of Range Images Using Hierachical Structure of Neural Networks (계층적 구조의 신경회로망을 이용한 거리영상의 분할)

  • 정인갑;현기호;이준재;하영호
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.10
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    • pp.123-129
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    • 1994
  • The segmentation of range image is essential to recognize the three dimensional object. Generally, surface curvature is well-known feature for segmentation and classification of the fange image, but it is sensitive to noies. In this paper, we propose the structure of hierarchical neural network using surface curvature for segmentation of range images. The hierarchical structure of neural networks is robust to noise and the result of segmentaion is better than conventional optimization method of single level.

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순서와 위상구조의 관계

  • 홍성사;홍영희
    • Journal for History of Mathematics
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    • v.10 no.1
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    • pp.19-32
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    • 1997
  • This paper deals with the relationship between the order structure and topological structure in the historical point of view. We first investigate how the order structure has developed along with the set theory and logic in the second half of the nineteenth century. After the general topology has emerged in the beginning of the twentieth century, two disciplines of the order theory and topology give each other a great deal of effect for their development via various dualities, compactifications by maximal filter spaces and Alexandroff's specialization order, which form eventually a fundamental setting for the development of the category theory or functor theory.

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Structure Optimization of Neural Networks using Rough Set Theory (러프셋 이론을 이용한 신경망의 구조 최적화)

  • 정영준;이동욱;심귀보
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.03a
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    • pp.49-52
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    • 1998
  • Neural Network has good performance in pattern classification, control and many other fields by learning ability. However, there is effective rule or systematic approach to determine optimal structure. In this paper, we propose a new method to find optimal structure of feed-forward multi-layer neural network as a kind of pruning method. That eliminating redundant elements of neural network. To find redundant elements we analysis error and weight changing with Rough Set Theory, in condition of executing back-propagation leaning algorithm.

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Classification by feedback structure and partitioning into acyclic subgraphs for a cyclic workflow graph

  • Choi, Yong-Sun
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2004.05a
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    • pp.718-721
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    • 2004
  • This paper introduces a novel method of partitioning a cyclic workflow graph into the subgraphs of acyclic flows. The way of iterative classification of nodes according to feedback structures and deriving subgraphs of acyclic flows is described with illustrative examples. The proposed method allows a cyclic workflow model to be analyzed further, if necessary, with several smaller subflows, which are all acyclic.

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