SVM

SVM 原理推导

机器学习就是找决策边界
1.have u ? if w * u + b 〉= 0 them is + 正样本（W*u =U的图影，b原点到边界的值）
if w * u >=c
if w * u +b <0 them is - 样本

2.yi(w * x +b) -1 >=0
yi(w * x + b) -1 =0

3. width =2/|w|

min 1/2 |w| sqr yi(w*x +b) -1 =0

4.key ida

L = 1/2 ||W||sqr - z[y]

kernel smo,qp,kkt

 

 

 dataset = numpy.loadtxt('path',delimiter=',')

x = dateset[:,0:8]
y = dataset[:,8]

dt.finllna(mean_cols)
all_dt = isnull().sum().sum()