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  • 正则化——逻辑回归

    逻辑回归的代价函数为

    [Jleft( heta  ight) =  - left[ {frac{1}{m}sumlimits_{i = 1}^m {{y^{left( i ight)}}log {h_ heta }left( {{x^{left( i ight)}}} ight) + left( {1 - {y^{left( i ight)}}} ight)log left( {1 - {h_ heta }left( {{x^{left( i ight)}}} ight)} ight)} } ight]]

    正则化后

    [Jleft( heta  ight) =  - left[ {frac{1}{m}sumlimits_{i = 1}^m {{y^{left( i ight)}}log {h_ heta }left( {{x^{left( i ight)}}} ight) + left( {1 - {y^{left( i ight)}}} ight)log left( {1 - {h_ heta }left( {{x^{left( i ight)}}} ight)} ight)} } ight] + frac{lambda }{{2m}}sumlimits_{j = 1}^n { heta _j^2} ]

    此时梯度下降算法为

    重复{

    [{ heta _0}: = { heta _0} - alpha left[ {frac{1}{m}sumlimits_{i = 1}^m {left( {{h_ heta }left( {{x^{left( i ight)}}} ight) - {y^{left( i ight)}}} ight)x_0^{left( i ight)}} } ight]]

    [{ heta _j}: = { heta _j} - alpha left[ {frac{1}{m}sumlimits_{i = 1}^m {left( {{h_ heta }left( {{x^{left( i ight)}}} ight) - {y^{left( i ight)}}} ight)x_j^{left( i ight)} + frac{lambda }{m}{ heta _j}} } ight]left( {j = 1,2,...,n} ight)]

    }

    (注意:区分逻辑回归与线性回归的h(x))

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  • 原文地址:https://www.cnblogs.com/qkloveslife/p/9866515.html
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