• Title, Summary, Keyword: pattern classification

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Ontology-based Fuzzy Classifier for Pattern Classification (패턴분류를 위한 온톨로지 기반 퍼지 분류기)

  • Lee, In-K.;Son, Chang-S.;Kwon, Soon-H.
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.6
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    • pp.814-820
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    • 2008
  • Recently, researches on ontology-based pattern classification have been tried out in many fields. However, in most of the researches, the ontology which represents the knowledge about pattern classification is just referred during the processes of the pattern classification. In this paper, we propose ontology-based fuzzy classifier for pattern classification which is extended from the fuzzy rule-based classifier In order to realize the proposed classifier, we construct an ontology by conceptualizing the method of fuzzy rule-based pattern classification and generate ontology inference rules for pattern classification. Lastly, we show the validity o) the proposed classifier through the experiment of pattern classification on the Fisher's IRIS dataset.

Negative Selection Algorithm for DNA Pattern Classification

  • Lee, Dong-Wook;Sim, Kwee-Bo
    • 제어로봇시스템학회:학술대회논문집
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    • pp.190-195
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    • 2004
  • We propose a pattern classification algorithm using self-nonself discrimination principle of immune cells and apply it to DNA pattern classification problem. Pattern classification problem in bioinformatics is very important and frequent one. In this paper, we propose a classification algorithm based on the negative selection of the immune system to classify DNA patterns. The negative selection is the process to determine an antigenic receptor that recognize antigens, nonself cells. The immune cells use this antigen receptor to judge whether a self or not. If one composes ${\eta}$ groups of antigenic receptor for ${\eta}$ different patterns, these receptor groups can classify into ${\eta}$ patterns. We propose a pattern classification algorithm based on the negative selection in nucleotide base level and amino acid level. Also to show the validity of our algorithm, experimental results of RNA group classification are presented.

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Efficient Implementing of DNA Computing-inspired Pattern Classifier Using GPU (GPU를 이용한 DNA 컴퓨팅 기반 패턴 분류기의 효율적 구현)

  • Choi, Sun-Wook;Lee, Chong-Ho
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.7
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    • pp.1424-1434
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    • 2009
  • DNA computing-inspired pattern classification based on the hypernetwork model is a novel approach to pattern classification problems. The hypernetwork model has been shown to be a powerful tool for multi-class data analysis. However, the ordinary hypernetwork model has limitations, such as operating sequentially only. In this paper, we propose a efficient implementing method of DNA computing-inspired pattern classifier using GPU. We show simulation results of multi-class pattern classification from hand-written digit data, DNA microarray data and 8 category scene data for performance evaluation. and we also compare of operation time of the proposed DNA computing-inspired pattern classifier on each operating environments such as CPU and GPU. Experiment results show competitive diagnosis results over other conventional machine learning algorithms. We could confirm the proposed DNA computing-inspired pattern classifier, designed on GPU using CUDA platform, which is suitable for multi-class data classification. And its operating speed is fast enough to comply point-of-care diagnostic purpose and real-time scene categorization and hand-written digit data classification.

Understanding Cold and Hot Pattern Classification Based on Systems Biology (시스템 생리학에 기반한 한열 변증의 이해)

  • Lee, Soojin
    • Journal of Physiology & Pathology in Korean Medicine
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    • v.30 no.6
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    • pp.376-384
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    • 2016
  • Systems biology is an emerging field aiming at a systems level understanding of living organisms and focusing on the characteristics of the whole network of them. The emergence of systems biology is partly because of the availability of huge amounts of data on organisms and the extensive support of computational technologies as the tools for understanding complex biological systems. The scientific understanding of Korean medicine has been obstructed because of the lack of proper methods examining the complex nature and the unique property of it. However, systems biology could give a chance understanding Korean medicine objectively and scientifically. Pattern classification is a unique tool of Korean medicine to diagnose and treat patients and systems biology would give a useful tool to interpret pattern classification. Various omics technologies has been used to explain the relations between pattern classification and biological factors and then many characteristics of pattern classification in various diseases have been discovered. Therefore, pattern classification could be a bridge to understand the features and differences of western medicine and Korean medicine and it could be a basis to develop pattern-based personalized medicine.

Design and Implementation of Intelligent Agent System for Pattern Classification

  • Kim, Dae-su;Park, Ji-hoon;Chang, Jae-khun;Na, Guen-sik
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.7
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    • pp.598-602
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    • 2001
  • Recently, due to the widely use of personal computers and internet, many computer users requested intelligent system that can cope with various types of requirements and user-friendly interfaces. Based on this background, researches on the intelligent agent are now activating in various fields. In this paper, we modeled, designed and implemented an intelligent agent system for pattern classification by adopting intelligent agent concepts. We also investigated the pattern classification method by utilizing some pattern classification algorithms for the common data. As a result, we identified that 300 3-dimensional data are applied to three pattern classification algorithms and returned correct results. Our system showed a distinguished user-friendly interface feature by adopting various agents including graphic agent.

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A Pattern Classification of HDD (Hard Disk Drive) Defect Distribution Using Fuzzy Inference (퍼지 추론을 이용한 HDD (Hard Disk Drive) 결함 분포의 패턴 분류)

  • Moon Un-Chul;Kwon Hyun-Tae
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.54 no.6
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    • pp.383-389
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    • 2005
  • This paper proposes a pattern classification algorithm for the defect distribution of Hard Disk Drive (HDD). In the HDD production, the defect pattern of defective HDD set is important information to diagnosis of defective HDD set. In this paper, 5 characteristics are determined for the classification to six standard defect pattern classes. A fuzzy inference system is proposed, the inputs of which are 5 characteristic values and the outputs are the possibilities that the input pattern is classified to standard patterns. Therefore, classification result is the pattern with maximum possibility. The proposed algorithm is implemented with the PC system for defective HDD sets and shows its effectiveness.

Pattern Classification Based on the Selective Perception Ability of Human Beings (인간 시각의 선택적 지각 능력에 기반한 패턴 분류)

  • Kim Do-Hyeon;Kim Kwang-Baek;Cho Jae-Hyun;Cha Eui-Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.2
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    • pp.398-405
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    • 2006
  • We propose a pattern classification model using a selective perception ability of human beings. Generally, human beings recognize an object by putting a selective concentration on it in the region of interest. Much better classification and recognition could be possible by adapting this phenomenon in pattern classification. First, the pattern classification model creates some reference cluster patterns in a usual way. Then it generates an SPM(Selective Perception Map) that reflects the mutual relation of the reference cluster patterns. In the recognition phase, the model applies the SPM as a weight for calculating the distance between an input pattern and the reference patterns. Our experiments show that the proposed classifier with the SPM acquired the better results than other approaches in pattern classification.

Pattern Classification with the Analog Cellular Parallel Processing Networks (아날로그 셀룰라 병렬 처리 회로망(CPPN)을 이용한 Pattern Classification)

  • 오태완;이혜정;김형석
    • Proceedings of the IEEK Conference
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    • pp.2367-2370
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    • 2003
  • A fast pattern classification algorithm with Cellular Parallel Processing Network-based dynamic programming is proposed. The Cellular Parallel Processing Networks is an analog parallel processing architecture and the dynamic programming is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast Pattern classification with optimization is formed. On such CPPN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

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Fast Pattern Classification with the Multi-layer Cellular Nonlinear Networks (CNN) (다층 셀룰라 비선형 회로망(CNN)을 이용한 고속 패턴 분류)

  • 오태완;이혜정;손홍락;김형석
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.52 no.9
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    • pp.540-546
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    • 2003
  • A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

Learning Networks for Learning the Pattern Vectors causing Classification Error (분류오차유발 패턴벡터 학습을 위한 학습네트워크)

  • Lee Yong-Gu;Choi Woo-Seung
    • Journal of the Korea Society of Computer and Information
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    • v.10 no.5
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    • pp.77-86
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    • 2005
  • In this paper, we designed a learning algorithm of LVQ that extracts classification errors and learns ones and improves classification performance. The proposed LVQ learning algorithm is the learning Networks which is use SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of LVQ. To extract pattern vectors which cause classification errors, we proposed the error-cause condition, which uses that condition and constructed the pattern vector space which consists of the input pattern vectors that cause the classification errors and learned these pattern vectors , and improved performance of the pattern classification. To prove the performance of the proposed learning algorithm, the simulation is performed by using training vectors and test vectors that are Fisher' Iris data and EMG data, and classification performance of the proposed learning method is compared with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional classification.

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