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PUBLIC MARKS from ogrisel with tags tutorial & "machine learning"

October 2008

Conditional Random Fields

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Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices. The underlying idea is that of defining a conditional probability distribution over label sequences given a particular observation sequence, rather than a joint distribution over both label and observation sequences. The primary advantage of CRFs over hidden Markov models is their conditional nature, resulting in the relaxation of the independence assumptions required by HMMs in order to ensure tractable inference. Additionally, CRFs avoid the label bias problem, a weakness exhibited by maximum entropy Markov models (MEMMs) and other conditional Markov models based on directed graphical models. CRFs outperform both MEMMs and HMMs on a number of real-world tasks in many fields, including bioinformatics, computational linguistics and speech recognition.

August 2008

Dbn_Tutorial

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Topics: Energy models, causal generative models vs. energy models in overcomplete ICA, contrastive divergence learning, score matching, restricted Boltzmann machines, deep belief networks

May 2007

obousquet - ML Videos

Online videos of talks or lectures about Machine Learning related topics

ogrisel's TAGS related to tag tutorial

ai +   bayesian +   cell +   conditional random fields +   crf +   DBN +   deep learning +   generative +   inference +   jackrabbit +   java +   jcr +   knowledge extraction +   machine learning +   nlp +   online +   opennlp +   programming +   ps3 +   RBM +   theory +   thread +   video +   workshop +