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Neural Network Modeling

교육 기간 : 2일(12시간)

교육 대상
수학적인 배경지식을 가지고 있는 데이터 분석가 및 모델러

과정 소개
이 과정은 Multi-Layer Perceptron과 Radial Basis Function의 두 가지의 신경망 분석 알고리즘에 대해 배웁니다. 신경망 모델의 이론과 실제 모델 생성 이슈를 포함하며, 적절한 신경망 구조를 선택하고 일반적인 training 기법을 결정하는 방법 및 NEURAL Procedure를 이용하여 신경망 분석을 하는 방법까지 학습하게 됩니다.

선수 지식
기본적인 통계분석 과정 및 데이터마이닝에 대한 개념 및 Enterprise Miner 사용에 대한 선수지식을 필요로 합니다.

교육 내용

  • Multilayer Perceptron과 Radial Basis Function 신경망 분석 설계
    • NLIN procedure 을 이용한 nonlinear regression 분석
    • EG procedure 을 이용한 polynomial regression 분석
    • GPLOT procedure을 이용한 nonparametric regression 분석
  • 적절한 신경망 구조와 Training 방법 선택
    • multilayer perceptrons 설계
    • normalized radial basis function networks 설계
  • 신경망 학습
    • Error Functions의 통계적 이론
    • 신경망 분석의 과적합을 피하는 방법
  • The NEURAL Procedure
    • weight interpretation을 이용한 변수선택
    • sensitivity-based pruning을 이용한 변수선택
  • Hybrid Networks


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