• 제목, 요약, 키워드: pattern classification

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

  • 이인근;손창식;권순학
    • 한국지능시스템학회논문지
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    • v.18 no.6
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    • pp.814-820
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    • 2008
  • 최근, 패턴분류에 온톨로지를 이용하려는 연구가 다양한 분야에서 시도되고 있다. 그러나 대부분의 이러한 연구에서는 패턴분류 관련 지식을 표현한 온톨로지지가 패턴분류 과정에서 단순히 참조되는 수준에 머물고 있다. 본 논문에서는 퍼지 규칙기반 분류기를 확장한 온톨로지 기반 퍼지 분류기를 제안한다. 이를 위해 퍼지규칙 기반 패턴분류 방법을 개념화하여 온톨로지를 구성하고, 패턴분류를 위한 온톨로지 추론 규칙을 생성한다. 그리고 IRIS 데이터집합의 패턴분류 실험을 통해 온톨로지 기반 퍼지 분류기의 타당성을 보인다.

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

  • 최선욱;이종호
    • 전기학회논문지
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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.

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

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

  • 문현철;권현태
    • 대한전기학회논문지:시스템및제어부문D
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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.

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

  • 김도현;김광백;조재현;차의영
    • 한국정보통신학회논문지
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    • v.10 no.2
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    • pp.398-405
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    • 2006
  • 인간은 관심을 가지고 있는 영역(ROI)에 대하여 선택적으로 주의를 집중하여 사물의 특징 을 인식하게 된다. 본 연구에서는 이러한 인간의 선택적 지각 능력을 적용한 패턴 분류 모델을 제안한다. 먼저 일반적인 클러스터링 알고리즘에 의해 입력 패턴들을 대략적으로 분류하여 참조 클러스터 패턴을 형성하고, 생성된 클러스터의 참조 패턴들을 상호 연관시켜 선택적 지각맵(SPM : Selective Perception Map)을 구성한다. 패턴 분류 및 인식 과정에서는 생성된 SPM을 입력 패턴과 참조 패턴과의 거리 계산에서 가중치로 적용함으로써 인간의 선택적 지각 능력을 패턴 분류에 반영하게 된다. 다양하게 변형된 인쇄체 숫자 및 필기체 숫자 데이터(MNIST)를 통해 실험해 본 결과 SPM을 사용한 패턴 분류 모델이 효과적임을 증명하였다.

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

  • 오태완;이혜정;김형석
    • 대한전자공학회:학술대회논문집
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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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다층 셀룰라 비선형 회로망(CNN)을 이용한 고속 패턴 분류 (Fast Pattern Classification with the Multi-layer Cellular Nonlinear Networks (CNN))

  • 오태완;이혜정;손홍락;김형석
    • 대한전기학회논문지:시스템및제어부문D
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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.

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

  • 이용구;최우승
    • 한국컴퓨터정보학회논문지
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    • v.10 no.5
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    • pp.77-86
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    • 2005
  • 본 논문에서는 분류오차를 추출하고 학습하여 분류성능을 개선하는 LVQ 학습 알고리즘을 설계하였다. 제안된 LVQ학습 알고리즘은 초기기준백터의 학습을 위해 SOM을 이용하고, LVQ 출력뉴런의 부류지정을 위하여 out-star 학습법을 사용하는 학습네트워크이다. 분류오차가 발생되는 패턴백터로 추출하기 위하여 오차유발조건을 제안하였고, 이 조건을 이용하여 분류오차를 유발시키는 입력패턴벡터로 구성되는 패턴백터공간을 구성하여 분류오차가 발생되는 패턴백터를 학습시키므로 분류오차수를 감소시키고, 패턴분류성능을 개선하였다. 제안된 학습알고리즘의 성능을 검증하기 위하여 Fisher의 Iris 데이터와 EMG 데이터를 학습백터 및 시험 백터로 사용하여 시뮬레이션 하였고, 제안된 학습방식의 분류 성능은 기존의 LVQ와 비교되어 기존의 학습방식보다 우수한 분류성공률을 확인하였다.

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