effective large scale stereo matching，用于快速立体匹配，代码量比较大，可以直接用在工程中。

Large Margin Rank Boundaries for Ordinal Regression

清晰 彩色 When most people hear “Machine Learning,” they picture a robot: a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here. In fact, it has been around for decades in some specialized applications, such as Optical Character Recognition (OCR). But the first ML application that really became mainstream, improving the lives of hundreds of millions of people, took over the world back in the 1990s: it was the spam filter. Not exactly a self-aware Skynet, but it does technically qualify as Machine Learning (it has actually learned so well that you seldom need to flag an email as spam anymore). It was followed by hundreds of ML applications that now quietly power hundreds of products and features that you use regularly, from better recommendations to voice search.

来自于GOOGLE的mapreduce的开山之作，此文是原英文的中文版本，希望能互相参照，加深理解

It would be ludicrous to attempt to erect a 50 story oce building using the same materials and techniques a carpenter would use to build a single family home.

code for Large Scale Metric Learning from Equivalence Constraints