## *Structured Prediction* Fall 2013

Reading list
- Axiomatic Derivation of the Principle of Maximum Entropy and the Principle of Minimum Cross-Entropy
- a maximum entropy approach to species distribution modeling
- A New Interpretation of Information Rate
- Conditional random fields-Probabilistic models for segmenting and labeling sequence data
- Discriminative Probabilistic Models for Relational Data
- Kernel Conditional Random Fields－Representation and Clique Selection
- Kernel Logistic Regression and the Import Vector Machine
- Learning Structured Prediction Models: A Large Margin Approach
- Maximum Margin Planning
- Multiscale Conditional Random Fields for Image Labeling
- Shallow Parsing with Conditional Random Fields
- Support Vector Machine Learning for Interdependent and Structured Output Spaces
- Convex Optimization. Chapter 2-5

Supplements:
- Calculus of Variations and Optimal Control Theory
- Functional Analysis, Calculus of Variations and Optimal Control
- Predicting structured data

Final project: implementing conditional random field for structured prediction