After a preliminary article on hidden Markov models, some months ago I had finally posted the article on Hidden Conditional Random Fields (HCRF) on CodeProject. The HCRF is a discriminative model, forming the generative-discriminative pair with the hidden Markov model classifers.
This CodeProject article is a second on a series of articles about sequence classification, the first being about Hidden Markov Models. I’ve used this opportunity to write a little about generative versus discriminative models, and also provide a brief discussion on how Vapnik’s ideas apply to these learning paradigms.
All the code available on those articles are also available within the Accord.NET Framework. Those articles provide good examples on how to use the framework and can be regarded as a practical implementation on how to use those models with the framework.
Complete framework documentation can be found live at the project’s website, as well as in the framework’s GitHub wiki. The framework has now been referred on 30+ publications over the years, and several more are already in the works, by me and users around the world.
Talking about publications, the framework has been used within my own research on Computer Vision. If you need help in understanding the inner workings of the HCRF, a more visual explanation on the HCRF derivation can also be found at the presentation I gave on Iberamia 2012 about Fingerspelling Recognition with Support Vector Machines and Hidden Conditional Random Fields.
- Souza, C. R., Pizzolato, E. B., Anjo, M. S.; Fingerspelling Recognition with Support Vector Machines and Hidden Conditional Random Fields . Advances in Artificial Intelligence – IBERAMIA 2012. Lecture Notes in Computer Science Volume 7637, 2012, pp 561-570. DOI: 10.1007/978-3-642-34654-5_57 [Manuscript] [Slideshow]
An application to a more interesting problem, namely natural words drawn from Sign Languages using a Microsoft Kinect, has also been accepted for publication at the 9th International Conference on Machine Learning and Data Mining, MLDM 2013, and
will be available soon. Update: it is available at
- Souza, C. R., Pizzolato, E. B.; Sign Language Recognition with Support Vector Machines and Hidden Conditional Random Fields: Going from Fingerspelling to Natural Articulated Words . Machine Learning and Data Mining in Pattern Recognition, Lecture Notes in Computer Science Volume 7988, 2013, pp 84-98. DOI: 10.1007/978-3-642-39712-7_7 [Manuscript] [Slideshow]
As usual, hope you find it interesting!