Coursera Machine Learning Course note--regularization

This section is about regularization, in the optimization of the use of regularization, in class when the teacher a word, not too much explanation. After listening to this class,

To understand the difference between a good university and a pheasant university. In short, this is a very rewarding lesson.

First of all, we introduce the reason for regularization, simply say that the complex model with a simple model to express, as to how to say, there is a series of deduction hypothesis, very creative.

The second section explains in depth the Lagrange multipliers in the optimization, and puts forward the concept of argument error. On the relationship between multiplicative and regularization items: multiplicative sub-large, regularization of small, that is, c small, the simpler the model.

Although there is a picture, it still sounds very abstract.

The third section is about regularization and VC theory, more abstract, I do not understand what is said ...

Section four describes how to design a regularization term, three principles.

The next section will tell you how to pick a multiplier.

Reference: http://www.cnblogs.com/ymingjingr/p/4395596.html

Coursera Machine Learning Course note--regularization