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统计学习理论和 SVM( 支持向量机 ) PowerPoint PPT Presentation


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统计学习理论和 SVM( 支持向量机 ). 主要内容. 统计学习理论的核心内容 支持向量机 ( 1 )标准的最优分类面 ( 2 )广义最优分类面 ( 3 )变换到高维空间的支持向量机 感受. 统计学习理论的核心内容. 统计学习理论是小样本统计估计和预测学习的最佳理论。

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统计学习理论和 SVM( 支持向量机 )

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Svm

SVM()


Svm

  • 1

  • 2

  • 3


Svm


Svm

YX,P(X,Y)P(X,Y):l( independently drawn and identically distributed )train set


Svm

H=f(x, w) ,w.P(X,Y)R(w)()


Svm

train setRemp(w)():

Remp(w)R(w)w()Remp(w)R(w)Remp(w)R(w)()


Svm

f(x, w)()Remp(w)R(w)1-(01):


Svm

hH=f(x, w)VC, l.


Svm

() Remp(w) ,,Remp(w) 0

,, VCh,(h/l),R(w),(Overfitting).


Svm

Support Vector Machines


Svm

(SRM)


Svm

,,(Hyperplane) ( : H1,H2.)

W dot product b


Svm

Optimal Hyperplane Support Vector


Svm

Margin =

..(1)

H1

H2

..(2)


Svm

(2),(1)

Minimum:

Subject to:


Svm

  • (2) :


Svm

:

Lagrange


Svm


Svm


Svm


Thank

Thank!

2003-4-18


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