Log-linear Models and Conditional Random Fields
published: Nov. 19, 2008, recorded: October 2008, views: 119109
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Description
Log-linear models are a far-reaching extension of logistic regression, while con- ditional random fields (CRFs) are a special case of log-linear models suitable for so-called structured learning tasks. Structured learning means learning to predict outputs that have internal structure. For example, recognizing handwritten words is more accurate when the correlations between neighboring letters are used to reÞne predictions. This tutorial will provide a simple but thorough introduction to these new developments in machine learning that have great potential for many novel applications.
The tutorial will first explain what log-linear models are, with with concrete examples but also with mathematical generality. Next, feature-functions will be explained; these are the knowledge-representation technique underlying log-linear models. The tutorial will then present linear-chain CRFs, from the point of view that they are a special case of log-linear models. The Viterbi algorithm that makes inference tractable for linear-chain CRFs will be covered, followed by a discus- sion of inference for general CRFs. The presentation will continue with a general derivation of the gradient of log-linear models; this is the mathematical foundation of all log-linear training algorithms. Then, the tutorial will discuss two impor- tant special-case CRF training algorithms, one that is a variant of the perceptron method, and another one called contrastive divergence. Last but not least, the tu- torial will introduce publicly available software for training and using CRFs, and will explain a practical application of CRFs with hands-on detail.
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Reviews and comments:
Lecture notes for this tutorial can be found at the author's homepage:
http://cseweb.ucsd.edu/~elkan/250B/ci...
There's something missing in the Last video unfortunately. The Collins perceptron.
very informative tutorial! thanks a lot Prof Elkan!
Awesome lectures \m/
I also think it must be another lecture to cover whole materials
Excellent lecture.
This tutorial is really an excellent introduction to conditional random fields. The pace is slow enough for everything to be pretty clear.
Thanks a lot for putting this online. There where only five people at the actual tutorial, but 3000 views proves that a lot more people have benefited from the videos.
Thank you Professor Elkan. Clear and well-paced tutorial.
Thanks for Professor Elkan. Wonderful lecture!
Before watching the video, I knew nothing about CRF.
After the tutorial, I feel that I knew a lot.
Prof. Elkan, thank you very much for this lecture.
Bye,
michele.
Thank you Professor Elkan,
I am not a mathematician by training and you make much of this simple enough for me to understand. It is a great help to me!
Actually, i don't understand clearly when he said that we do not need to model p(x).
Thank you Professor Elkan, an awesome tutorial!
The best video on Conditional Random fields I have ever seen. Perfect for a beginner who has no idea what CRF is. I think one last video part 7 is missing because he hasn't ended the session in the 6th video.
thanks Elkan, now i'm more clear about crf, and i will go on to learn 2D crf structure.
thank you Elkan, you did very wonderful tutorial and uploading extend the benefit for anyone who search this kind of topic
Sorry, I cannot find the lecture notes. I think link is dead. Is there some alternative link?
Lecture Notes are available on the lecture website:
http://cseweb.ucsd.edu/~elkan/250B/
Really, Rakesh Aggarwal is sitting among four.
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A video helps me know a lot of things
Great lecture. I learned a lot. Thank you
-Burt | https://selltohouseguys.com/
Thank you for such a sweet tutorial – all this time later, I’ve found it and love the end result. I appreciate the time you spent sharing your skills https://www.recruitmentcave.com/news/...
Professor Elkan, thank you very much.
I am not a mathematician by training, yet you make much of this understandable for me. It is really beneficial to me!
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I love the valuable info provided in your article.
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Thank you Professor Elkan, for making it more easier to understand. It is an awesome tutorial! https://www.renovation-appartement-pa...
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