Logothe feasibility of machinelearning
Logo the feasibility of machine learning
LogoComponent of learningo Formalization- Input (输入） :X(customer application)think of it as deed dimension vector_ Output (输出）:Y(+1,-1)good/bad customer_TargetFunction(目标函数）：f:x→yideal credit approval formula
Logo Company Logo Component of learning Formalization – Input（输入）:X (customer application) think of it as deed dimension vector – Output（输出）:Y(+1,-1) good/bad customer – Target Function(目标函数) : f :x→y ideal credit approval formula
LogoComponent of learningo Formalization- Data (数据） : (x1,y1), (x2,Y2),.., (xn,Yn)historical records↓↓+_ Hypothesis(假设） ：g :x—→y为了得到目标函数的公式Formalization-Data (数据) () (32 (%)historical records1+F is unknown G is very much known-Hypothesis(假设）gx-y为了得到目标再数的公式actually we created it#Pe家园mpany0.PPTJIa.COM
Logo Company Logo Component of learning Formalization – Data（数据）: (𝑥1 , 𝑦1 ), (𝑥2 , 𝑦2 ),., (𝑥𝑛, 𝑦𝑛) historical records ↓ ↓ ↓ – Hypothesis(假设) ：g :x→y 为了得到目标函数的公式 F is unknown G is very much known actually we created it
LogoComponent of learningUNKNOWNNTARGETFUNCTIONf :x-→y↓+TRAINING EXAMPLES(x1,y1), (x2, y2),..., (xn,yn)FINALHYPOTHESISg(G hopefully approximates F)
Logo Company Logo Component of learning UNKNOWN TARGET FUNCTION f :x→y ↓ ↓ TRAINING EXAMPLES (𝑥1 , 𝑦1 ), (𝑥2 , 𝑦2 ),., (𝑥𝑛, 𝑦𝑛) FINAL HYPOTHESIS g (G hopefully approximates F)
LogoComponent of learningUNKNOWNNTARGETFUNCTIONf :x-→y↓+TRAINING EXAMPLES(x1,y1), (x2, y2),..., (xn,yn)FINALHYPOTHESISg(G hopefully approximates F)
Logo Company Logo Component of learning UNKNOWN TARGET FUNCTION f :x→y ↓ ↓ TRAINING EXAMPLES (𝑥1 , 𝑦1 ), (𝑥2 , 𝑦2 ),., (𝑥𝑛, 𝑦𝑛) FINAL HYPOTHESIS g (G hopefully approximates F)