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第九讲 Linear Regression

已有 2260 次阅读 2014-1-15 11:09 |个人分类:科研道路|系统分类:科研笔记

上一讲我们得到 learning can happen with target distribution $P(y|x)$ and low $E_{in}$ w.r.t. error measure.

1. Linear Regression Problem 

  linear regression hypothesis: $h(x) = w^T x$, $h(x)$: like perceptron, but without the $sign$.

  linear regression: find lines/hyperplanes with small residuals.

 

  The Error Measure:

  popular/historical error measure: squared error $err(\hat{y}; y) = (\hat{y} - y)^2$.


2. Linear Regression Algorithm




practical suggestion: use well-implemented $y$ routine instead of $(X^T X)^{-1} X^T$ for numerical stability when almost-singular.

Linear Regression Algorithm:


3. Generalization Issue




4. Linear Regression for Binary Classification







https://blog.sciencenet.cn/blog-507072-759302.html

上一篇:第八讲 Noise and Error
下一篇:第十讲 Logistic Regression

1 陆泽橼

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