Some references to start with conditional random fields
One interesting project that I am involved in these days involves certain problems in Intelligent Tutors. It turns out that perhaps one of the best ways to tackle them is by using Conditional Random Fields (CRFs). Many attempts to solving these problems still involve Hidden Markov Models (HMMs). Since I have never really been a Graphical Models guy (though I am always fascinated) so I found the going on studying CRFs quite difficult. Now that the survey is more or less over, here are my suggestions for beginners to go about learning them.
1.Log-Linear Models and Conditional Random Fields (Tutorial by Charles Elkan)
Log-linear Models and Conditional Random Fields
6 videos: Click on Image above to view
Two directions of approaching CRFs are especially useful to get a good perspective on their use. One of these is considering CRFs as an alternate to…
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