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Conditional Random Fields - A probabilistic graphical model Stefan Mutter
Motivation Bayesian Network Naive Bayes Markov Random Field Hidden Markov Model Logistic Regression Linear Chain Conditional Random Field General Conditional Random Field
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overview: directed graphical models Bayesian Network Naive Bayes Markov Random Field Hidden Markov Model Logistic Regression Linear Chain Conditional Random Field General Conditional Random Field
Bayesian Networks: directed graphical models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overview: undirected graphical models Bayesian Network Naive Bayes Markov Random Field Hidden Markov Model Logistic Regression Linear Chain Conditional Random Field General Conditional Random Field
Markov Random Field: undirected graphical models ,[object Object],[object Object],[object Object],[object Object], green  red
Markov Random Fields and CRFs ,[object Object],[object Object]
Overview: generative  discriminative models Bayesian Network Naive Bayes Markov Random Field Hidden Markov Model Logistic Regression Linear Chain Conditional Random Field General Conditional Random Field  
Generative models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Discriminative models ,[object Object],[object Object],[object Object],[object Object],[object Object],computed by inference conditional approach more freedom to fit data
Naive bayes and logistic regression (1) ,[object Object],[object Object],[object Object],LR is a MRF globally conditioned on X Use log-linear model as potential functions in CRFs LR is a very simple CRF
Naive bayes and logistic regression (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overview: sequence models Bayesian Network Naive Bayes Markov Random Field Hidden Markov Model Logistic Regression Linear Chain Conditional Random Field General Conditional Random Field
Sequence models: HMMs ,[object Object],[object Object],[object Object],[object Object],[object Object]
From HMMs to linear chain CRFs (1) ,[object Object],[object Object],[object Object],with
From HMMs to linear chain CRFs (2) ,[object Object],[object Object]
Linear chain conditional random fields ,[object Object],[object Object],with
Side trip: maximum entropy markov models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Side Trip: label bias problem ,[object Object],[object Object],[object Object],[object Object],Calculate:
Inference in a linear chain CRF ,[object Object],[object Object],[object Object],[object Object],where
Parameter estimation in general ,[object Object],[object Object],[object Object],[object Object]
Principles in parameter estimation ,[object Object],[object Object],[object Object],[object Object]
Application: gene prediction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary: graphical models
The end Questions ?
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object]

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PowerPoint Presentation - Conditional Random Fields - A ...

  • 1. Conditional Random Fields - A probabilistic graphical model Stefan Mutter
  • 2. Motivation Bayesian Network Naive Bayes Markov Random Field Hidden Markov Model Logistic Regression Linear Chain Conditional Random Field General Conditional Random Field
  • 3.
  • 4. Overview: directed graphical models Bayesian Network Naive Bayes Markov Random Field Hidden Markov Model Logistic Regression Linear Chain Conditional Random Field General Conditional Random Field
  • 5.
  • 6. Overview: undirected graphical models Bayesian Network Naive Bayes Markov Random Field Hidden Markov Model Logistic Regression Linear Chain Conditional Random Field General Conditional Random Field
  • 7.
  • 8.
  • 9. Overview: generative  discriminative models Bayesian Network Naive Bayes Markov Random Field Hidden Markov Model Logistic Regression Linear Chain Conditional Random Field General Conditional Random Field  
  • 10.
  • 11.
  • 12.
  • 13.
  • 14. Overview: sequence models Bayesian Network Naive Bayes Markov Random Field Hidden Markov Model Logistic Regression Linear Chain Conditional Random Field General Conditional Random Field
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 27.
  • 28.