机器学习 coursera【week7-9】

week07Support Vector Machines

7.1Large Margin Classification

it is a more cleaner and more powerful way to learn complex non-linear function

start from logistic regression(classification) 

7.2Mathematics behind large margin classification

SVM Decision Boundary: the distance between blue line and green line

7.3Kernels核函数

关于核函数的几个问题：

• 如何选择标记点？
• 如何得到这些标记点？
• 相似度方程是怎么样的？
• 能否用其他核函数来代替高斯核函数?

7.3.1Kernels I

introduction

高斯核函数的相似性

不同thegma对应的核函数图像

7.3.1Kernels II

C is equal to 1/lambda, so 

large C == small lambda, which means lower bias, high variance and overfitting 

small C == large lambda, which means high bias, low variance and underfitting