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  • 과거 관찰로 현재의 연속적인 변수(Variable)를 추출하는 것. 예) 가격
  • 이것이 선형 관계라면 선형 회귀 (Linear Regression)
  • 전체 오류( (predictionreality)2(\texttt{prediction}-\texttt{reality})^2)를 최소화하는 함수/변수를 찾자
  • 입력 xRDx \in \mathbb{R}^D (Features, Covariates, Contexts, Predictors, etc)
  • 출력 yRy \in \mathbb{R} (Responses, Targets, Outcomes, etc)
  • f:RDRf: \mathbb{R}^D \to \mathbb{R} with f(x)=w0+d=1Dwdxd=w0+wTxf(x) = w_0 + \sum^D_{d=1} w_d x_d = w_0 + w^T x (superscript T^T stands for transpose)
  • i.e., hyperplane, parameterized by w=[w1w2wD]Tw = [w_1 w_2 \cdots w_D]^T (weights, weight vector, parameter vector, etc)
  • bias w0w_0

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