Journal of the Institute of Electronics Engineers of Korea CI (전자공학회논문지CI)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Statistical Radial Basis Function Model for Pattern Classification

패턴분류를 위한 통계적 RBF 모델

  • Choi Jun-Hyeog (Division of Computer Science, Kimpo College) ;
  • Rim Kee-Wook (Knowledge Information & Industrial Engineering Department, Sunmoon Univ.) ;
  • Lee Jung-Hyun (School of Computer Science & Engineering, Inha Univ.)
  • 최준혁 (김포대학 컴퓨터계열) ;
  • 임기욱 (선문대학교 지식정보산업공학과) ;
  • 이정현 (인하대학교 컴퓨터 공학부)
  • Published : 2004.01.01
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Abstract

According to the development of the Internet and the pervasion of Data Base, it is not easy to search for necessary information from the huge amounts of data. In order to do efficient analysis of a large amounts of data, this paper proposes a method for pattern classification based on the effective strategy for dimension reduction for narrowing down the whole data to what users wants to search for. To analyze data effectively, Radial Basis Function Networks based on VC-dimension of Support Vector Machine, a model of statistical teaming, is proposed in this paper. The model of Radial Basis Function Networks currently used performed the preprocessing of Perceptron model whereas the model proposed in this paper, performing independent analysis on VD-dimension, classifies each datum putting precise labels on it. The comparison and estimation of various models by using Machine Learning Data shows that the model proposed in this paper proves to be more efficient than various sorts of algorithm previously used.

인터넷의 발달과 데이터베이스의 구축이 보편화됨에 따라 막대한 양의 데이터 속에서 의사 결정에 필요한 지식을 찾아내는 작업은 결코 쉬운 일이 아니다 본 논문에서는 대규모 데이터의 효율적인 분석을 위하여 지식의 탐사 이전에 데이터에 대한 축소 작업을 수행하기 위한 효과적인 차원 축소 전략에 의한 패턴분류 기법을 제안한다. 이를 위해 본 논문에서는 통계적학습 모형인 Support Vector Machine의 VC-dimension에 기반한 RBF 신경망 모형을 제안한다. 기존의 RBF 신경망 모형은 주로 퍼셉트론 모형의 전처리 작업만을 수행하지만 제안하는 신경망 모형은 VD-dimension과 연계한 독자적으로 데이터를 분석할 수 있는 능력을 갖춘 모형을 구축하고 이를 바탕으로 개체들을 정확한 레이블로 분류한다. 기계 학습 데이터를 이용하여 본 논문에서 제안하는 모형의 성능을 비교 평가한 결과 기존의 여러 분류 알고리즘에 비해 우수한 성능을 보임이 실험을 통해 확인되었다.

Keywords

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