The KIPS Transactions:PartB (정보처리학회논문지B)

Korea Information Processing Society (한국정보처리학회)
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Performance comparison of SVM and neural networks for large-set classification problems

대용량 분류에서 SVM과 신경망의 성능 비교

  • 이진선 (우석대학교 컴퓨터공학과) ;
  • 김영원 (전북대학교 대학원 컴퓨터정보학과) ;
  • 오일석 (전북대학교 전자정보공학부)
  • Published : 2005.02.01
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Abstract

In this paper, we analyzed and compared the performances of modular FFMLP(feedforward multilayer perceptron) and SVUT(Support Vector Machine) for the large-set classification problems. Overall, SVM dominated modular FFMLP in the correct recognition rate and other aspects Additionally, the recognition rate of SVM degraded more slowly than neural network as the number of classes increases. The trend of the recognition rates depending on the rejection rate has been analyzed. The parameter set of SVM(kernel functions and related variables) has been identified for the large-set classification problems.

이 논문은 대용량 분류 문제를 위한 모듈러 신경망(modular feedforward MLP)과 SVM(Support Vector Machine)의 성능을 비교 분석하였다. 전반적으로 SVM이 상당한 성능 차이로 우수함을 확인하였다. 또한 부류 수가 많아짐에 따라 SVM이 신경망보다 완만하게 성능 저하가 있음도 확인하였다. 또한 기각에 따른 정인식률 추이를 분석하였고, 대용량 분류에 적합한 SVM 파라메터(kernel 함수와 관련 변수들)를 도출하였다.

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References

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