<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[weitian_bnu的博客]]></title><description><![CDATA[专注，执着。]]></description><link>https://blog.youkuaiyun.com/weitian_bnu</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; weitian_bnu]]></copyright><item><title><![CDATA[CS229 Lecture Notes(4): Generative Learning Algorithm]]></title><link>https://blog.youkuaiyun.com/weitian_bnu/article/details/51179366</link><guid>https://blog.youkuaiyun.com/weitian_bnu/article/details/51179366</guid><author>weitian_bnu</author><pubDate>Mon, 18 Apr 2016 14:29:05 +0800</pubDate><description><![CDATA[Generative Learning Algorithm
discriminative learning algorithm: Algorithms try to learn p(y|x)p(y|x) directly or try to learn mappings f(x)f(x) directly from the space of inputs \mathcal{X} to the la]]></description><category></category></item><item><title><![CDATA[CS229 Lecture Notes(3): Generalized Linear Models]]></title><link>https://blog.youkuaiyun.com/weitian_bnu/article/details/51179356</link><guid>https://blog.youkuaiyun.com/weitian_bnu/article/details/51179356</guid><author>weitian_bnu</author><pubDate>Mon, 18 Apr 2016 14:27:55 +0800</pubDate><description><![CDATA[The exponential family
A class of distributions is in the exponential family if it can be written in the from p(y,η)=b(y)exp(ηTT(y)−a(η))p(y,\eta)=b(y)exp(\eta^TT(y)-a(\eta)) 
where:η\eta: the natural]]></description><category></category></item><item><title><![CDATA[CS229 Lecture Notes(2): Logistic Regression]]></title><link>https://blog.youkuaiyun.com/weitian_bnu/article/details/51179339</link><guid>https://blog.youkuaiyun.com/weitian_bnu/article/details/51179339</guid><author>weitian_bnu</author><pubDate>Mon, 18 Apr 2016 14:26:26 +0800</pubDate><description><![CDATA[Logistic Regression
Binary classification problem
Failure of OLS regression in binary classification problem:hard to define the threshold
no sense if y>1y>1 or y<0y<0
Hypothesis: hθ(x)=g(θTx)=11+e−θTxh]]></description><category></category></item><item><title><![CDATA[CS229 Lecture Note(1): Linear Regression]]></title><link>https://blog.youkuaiyun.com/weitian_bnu/article/details/51132552</link><guid>https://blog.youkuaiyun.com/weitian_bnu/article/details/51132552</guid><author>weitian_bnu</author><pubDate>Tue, 12 Apr 2016 13:33:55 +0800</pubDate><description><![CDATA[1. LMS Algorithm
The Ordinary Least Squares Regression Model: h(θ)=θTxh(\theta)=\theta^Tx
Cost Function: J(θ)=12∑mi=1(hθ(xi)−yi)2J(\theta)=\frac{1}{2}\sum_{i=1}^m(h_\theta(x^i)-y^i)^2
LMS (least mean s]]></description><category></category></item></channel></rss>