谁能推荐一个很好的教程条件随机场 [英] Anyone Recommend a Good Tutorial on Conditional Random Fields

问题描述

我一直在试图找到条件随机场的一个很好的教程，还没有找到一个没有开始发送我的大脑崩溃。我有一个很好的把握HMM的，我得到的歧视性和生成模式之间的区别......但作为然而我一直没能找到一个资源，可以给HMM的和CRF的这对我来说很有意义的一个很好的比较。任何援助将AP preciated。

I have been trying to find a good tutorial on Conditional Random Fields and have yet to find one that didn't start sending my brain into meltdown. I have a good grasp of HMM's, and I get the difference between discriminative and generative models ... but as of yet I have not been able to find a resource that can give a good comparison of HMM's and CRF's that makes sense to me. Any assistance would be appreciated.

推荐答案

一，我找到了最好的资源，实际上是在克里斯托弗·毕晓普的书的模式识别与机器学习的一个部分（我强烈建议，顺便说一句）关于马尔可夫随机域（控释肥是专门马尔可夫随机场）。它甚至有一个例子，我敢肯定，你现在已经注意到是令人难以置信的很难找到这个主题。现在，我必须规定，这部分不会给你控释肥的完全理解，但它有望 - 至少它为我做的 - 帮助您浏览那些奸诈的CRF教程

One of the best resources I've found is actually a section in Christopher Bishop's book Pattern Recognition and Machine Learning (which I highly recommend, by the way) regarding Markov Random Fields (CRFs are specialized Markov Random Fields.) It even has an example, which I'm sure you've noticed by now are incredibly hard to find for this subject. Now, I must stipulate that this section won't give you a complete understanding of CRFs, but it will hopefully - at least it did for me - help you navigate those treacherous CRF tutorials.

除此之外，我还没有发现什么，但头脑麻木关于这个问题的学术论文。这里有几个，我发现是有帮助的，虽然：

Aside from that, I haven't found anything but mind-numbing academic papers on the subject. Here are a few that I found to be helpful though:

• 介绍到条件随机域的关系学习 - 这一个是相当彻底。

• An Introduction to Conditional Random Fields for Relational Learning - This one is quite thorough.

条件随机域：概率模型进行分段和标签序列数据 - 这是最初列出的CRF框架的文件。我觉得有点更容易阅读比那些之后，随同。它也比较控释肥到HMM模型和MEMMs（图包括在内。）

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data - This is the paper which originally outlined the CRF framework. I find it a bit easier to read than the ones that came after it. It also compares CRFs to HMMs and MEMMs (diagrams included.)

对不起，这就是我可以作出贡献。我还在努力掌握控释肥自己。

Sorry, that's all I can contribute. I'm still trying to master CRFs myself.

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