本文探讨了机器学习中监督学习与无监督学习的核心概念。通过Andrew Ng的观点，文章详细解释了回归、分类及特征选择在监督学习中的作用，同时讨论了支持向量机在处理无限特征集时的应用。对于无监督学习，文章介绍了其在数据结构发现中的应用，如Google News如何使用聚类算法自动分类新闻故事。
监督学习
supervised learning, Andrew Ng doesn’t give its definition directly. He use two examples:Regression and Classification.
Still classification
More than one feature
Therefore, in my opinion, if you want your decision boundary more specific, how to choose your features must be important.
As Andrew Ng said, it turns out for some learning problems, what you really want is not to use, like three or five features. Instead, you want to use an infinite number of features. So, how do you use computer to store an infinite number of features when your computer is gonna run out of memory.->Suported vectorized machine
In the light of all of above, supervised learning is datasets plus right answer.
无监督学习
unsupervised learing-Here is the data ,can you find some instructure in the data?
one example where clustering is used is in Google News.
What Google News has done is to look for tens of thousands of news stories and then automatically cluster them together. So the news stories that all about the same topic get displayed together.
Andrew Ng also gives an example successfuly separating two mixed micro voice.
扫一扫
1412
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
