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直到看到这本书 写给大家看的设计书,也许英文名称表达的更加清晰 The Non-Designer’s Design Book,给业余选手看的设计书。 1、确定分类器的分类错误率$ epsilon$ = 错误分类样本$ ast D {t}$/所有样本$ ast D {t}$,从而可以确定分类器权重. Alpha = frac{1}{2} ln( frac{1- epsilon}{ epsilon})$. 2、根据分类器权重,确定样本权重,加入有m个样本,初始样本权重为 $D 0 = frac{1}{m}$,迭代的权重为 $ D {t 1}(i) = frac{D t(i) * E t(i)}{Z t}$. E t(i)$的值,当样本在分类器上分类正确时,等于$e {- alpha t}$,分类错误时等于$e { alpha t}$, $Z t$只为标准化$D t$. 对于均匀噪声 uniform noise 比较脆弱。 Refer to Boosting finalRpdf. Life is boring, when you are concentrating on something.

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Gill Xiao | gillxiao.com Reviews
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直到看到这本书 写给大家看的设计书,也许英文名称表达的更加清晰 The Non-Designer’s Design Book,给业余选手看的设计书。 1、确定分类器的分类错误率$ epsilon$ = 错误分类样本$ ast D {t}$/所有样本$ ast D {t}$,从而可以确定分类器权重. Alpha = frac{1}{2} ln( frac{1- epsilon}{ epsilon})$. 2、根据分类器权重,确定样本权重,加入有m个样本,初始样本权重为 $D 0 = frac{1}{m}$,迭代的权重为 $ D {t 1}(i) = frac{D t(i) * E t(i)}{Z t}$. E t(i)$的值,当样本在分类器上分类正确时,等于$e {- alpha t}$,分类错误时等于$e { alpha t}$, $Z t$只为标准化$D t$. 对于均匀噪声 uniform noise 比较脆弱。 Refer to Boosting finalRpdf. Life is boring, when you are concentrating on something.
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gill xiao,读书笔记 证析,分类于 books,第一周看完设计书后就想看本和工作相关的书,然后就想起了这本 证析,接触这本书源于新浪媒介策略总监的推荐,还写了几条书评,评价特别高,于是上市后第一时间购入,68的原价也算是大出血,直到这周末重新拿起,才知道自己错过了什么,强烈推荐,推荐所有和数据打交道的人阅读此书,不管是什么角色 从业者、决策者,两年前大数据是热点,现在的大数据是臭虫,此书虽然副标题提到了大数据,但是不是,这不是本技术书,对于被大数据概念轰炸了好几年的互联网人士来说,无阅读门槛
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直到看到这本书 写给大家看的设计书,也许英文名称表达的更加清晰 The Non-Designer’s Design Book,给业余选手看的设计书。 1、确定分类器的分类错误率$ epsilon$ = 错误分类样本$ ast D {t}$/所有样本$ ast D {t}$,从而可以确定分类器权重. Alpha = frac{1}{2} ln( frac{1- epsilon}{ epsilon})$. 2、根据分类器权重,确定样本权重,加入有m个样本,初始样本权重为 $D 0 = frac{1}{m}$,迭代的权重为 $ D {t 1}(i) = frac{D t(i) * E t(i)}{Z t}$. E t(i)$的值,当样本在分类器上分类正确时,等于$e {- alpha t}$,分类错误时等于$e { alpha t}$, $Z t$只为标准化$D t$. 对于均匀噪声 uniform noise 比较脆弱。 Refer to Boosting finalRpdf. Life is boring, when you are concentrating on something.

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about // Gill Xiao

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Linear Regression part one.

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Linear Regression part one.

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Linear Regression part one // Gill Xiao

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Linear Regression part one. Y = theta 0 theta 1 x 1 theta 2 x 2 $. 假设$x 0 = 1$,因此可以以矩阵形式改写为. Y = Theta T X$. J( theta) = sum {i=0} n( theta i x i - y i) 2$. Min { rm theta} J( theta) = min { rm theta} sum {i=0} n( theta i x i - y i) 2$. 梯度下降的原理就是$J( theta)$对$ theta$进行求导,得到当前的梯度,也就是下降最快的斜率,然后修正$ theta$。 Theta = Theta - gamma frac{dJ}{d theta}$. J( theta) = sum {i=0} nw i( theta i x i -y i) 2$. 相比普通的线性回归为题,增加的局部加权参数为 $w i = exp(- frac{(x-x i) 2}{2 Gamma 2})$. X = x - frac{f(x)}{f { prime}(x)}$.

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直到看到这本书 写给大家看的设计书,也许英文名称表达的更加清晰 The Non-Designer’s Design Book,给业余选手看的设计书。 1、确定分类器的分类错误率$ epsilon$ = 错误分类样本$ ast D {t}$/所有样本$ ast D {t}$,从而可以确定分类器权重. Alpha = frac{1}{2} ln( frac{1- epsilon}{ epsilon})$. 2、根据分类器权重,确定样本权重,加入有m个样本,初始样本权重为 $D 0 = frac{1}{m}$,迭代的权重为 $ D {t 1}(i) = frac{D t(i) * E t(i)}{Z t}$. E t(i)$的值,当样本在分类器上分类正确时,等于$e {- alpha t}$,分类错误时等于$e { alpha t}$, $Z t$只为标准化$D t$. 对于均匀噪声 uniform noise 比较脆弱。 Refer to Boosting finalRpdf. Life is boring, when you are concentrating on something.

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