Linear Predictor (minimal Perceptron)
h(x)=Σi=0dθixi=θTxin Linear Regression there is always global optimum
- update θ - Gradient Descent like algorithms
- add gradient of derivative of cost function J
J(θ)=21Σi=1n(hθ(x(i))−y(i))2=21(Xθ−y)T(Xθ−y)θ:=θ+αΣ(yi−hθ(xi))xi- Closed form - solve equation - assume XTX is invertible
θ=(XTX)−1XTy