BetaCZH的博客

Machine Learning Notesthis is the summary courses of ML on cousera by Andrew Ng 1.What is Machine Learning?**Definition:**A computer program is said to learn from experience E with respect to some ta

Machine Learning Notes II1.Classification Focus on Binary classification problem Hypothesis representation using Sigmoid Function,also called logistic function The function looks like this->

support vector machine.第一次了解svm是在Andrew的公开课上，可惜他讲的比较浅，仅仅介绍了一点基础知识，包括一些术语都没有涉及。然而最近在看machine learning in action里面也有一章专门讲svm的，可惜英语不过关只能先找中文的看看然后才能看英文的。。。于是我看了许多关于（不算许多）svm的博客，发现这东西博大精深，可挖的东西很多，特此写下来总结一下，

Machine Learning Notes III1.Neural networks The most important thing we should make clear is the map in this neural networks model.Here’s the model-> There’s no doubt that the image above is a qui

模型评估与选择1. empirical error && overfitting(经验误差和过拟合)分类错误的样本数占样本总数的比例成为 error rate(错误率) 相应的，分类正确的称为 accuracy(精确度) 对于学习器的实际预测输出和样本的真实输出差异称为 error(误差)training error|empirical error(训练误差|经验误差) 在训练集上的误差g