Implementation of discrete hidden markov model for sequence classification in C++ using Eigen


Conditional Random Fields: A Beginner’s Survey

Some references to start with conditional random fields

Onionesque Reality

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.

Tutorialsand Theory

1.Log-Linear Models and Conditional Random Fields (Tutorial by Charles Elkan)

Log-linear Models and Conditional Random Fields
Charles Elkan

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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Polynomial Approximation of 2D image patch -Part 2

A more detailed analysis supported by code for polynomial least square approximation of a 2D image patch.The code can be found at git repository