Structured Prediction Fall 2013

Reading list
  1. Axiomatic Derivation of the Principle of Maximum Entropy and the Principle of Minimum Cross-Entropy
  2. a maximum entropy approach to species distribution modeling
  3. A New Interpretation of Information Rate
  4. Conditional random fields-Probabilistic models for segmenting and labeling sequence data
  5. Discriminative Probabilistic Models for Relational Data
  6. Kernel Conditional Random Fields－Representation and Clique Selection
  7. Kernel Logistic Regression and the Import Vector Machine
  8. Learning Structured Prediction Models: A Large Margin Approach
  9. Maximum Margin Planning
  10. Multiscale Conditional Random Fields for Image Labeling
  11. Shallow Parsing with Conditional Random Fields
  12. Support Vector Machine Learning for Interdependent and Structured Output Spaces
  13. Convex Optimization. Chapter 2-5
Supplements:
  1. Calculus of Variations and Optimal Control Theory
  2. Functional Analysis, Calculus of Variations and Optimal Control
  3. Predicting structured data
Final project: implementing conditional random field for structured prediction