A conditional random field (CRF) is a type of discriminative undirected probabilistic graphical model. It is most often used for labeling or parsing of sequential data, such as natural language text or biological sequences[1] and computer vision[2] . Specifically, CRFs find applications in shallow parsing[3] , named entity recognition[4] and gene finding, among other tasks, being an alternative to the related hidden Markov models.
http://www.inference.phy.cam.ac.uk/hmw26/crf/
The primary advantage of CRFs over hidden Markov
models is their conditional nature, resulting in the relaxation of the indepen-
dence assumptions required by HMMs in order to ensure tractable inference.
CRFs outperform both MEMMs
and HMMs on a number of real-world sequence labeling tasks
Nando de Freitas
http://www.cs.ubc.ca/~nando/
http://www-stat.stanford.edu/~tibs/ElemStatLearn/download.html
- Deep Learning Website.
- Geoff Hinton's Website.
- Yann Lecun's Website.
- Andrew Ng's Website.
- Jason Weston's Website.
- Russ Salakhutdinov's Website.
- The book of Kevin Murphy.
- Hastie, Tibshirani and Friedman: The elements of statistical learning.
- Machine learning video lectures
- Why stats: NYTimes article
- The following handout should help you with linear algebra revision: PDF
David JC MacKay
http://www.inference.phy.cam.ac.uk/mackay/itila/
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