1. Learning Coordination Classifiers
Guo, Greiner, and Schuurmans
University of Alberta
Presented by Nick Rizzolo

2. Outline Standard assumptions about classification
What’s a “coordination classifier”?
How is it trained?
How is it evaluated?
How is this approach justified?
Experiments
9/13/05
AIML Seminar

3. Standard Classification Assumptions
Input:
Output:
Training and testing data are independent and identically distributed (i.i.d.) f
9/13/05
AIML Seminar

4. Coordination Classification
Pair examples
Multi-class f
Coordination
9/13/05
AIML Seminar

5. Training Amount of training examples is squared
Maximum likelihood, logistic regression, naïve Bayes, Bayes networks, neural networks, etc.
Trained classifier makes dependent associations
9/13/05
AIML Seminar

6. Evaluation HMM, CRF, voting, …
What the authors did: Markov Random Field
Training examples
Testing examples
9/13/05
AIML Seminar

7. Evaluation (cont’) Full network is impractical Just train-test edges
Analogous to kernel based algorithms
Easy to compute most likely labeling
Just test-test edges
Analogous to ensemble (voting) methods
Probabilistic inference is expensive
Random edge subsampling
9/13/05
AIML Seminar

8. Rationale x1 x2 y1 y2 test examples true conditional model learned
9/13/05
AIML Seminar

9. Experiment 1
Logistic regression, belief propagation, only test edges, 18 edges per example
9/13/05
AIML Seminar

10. Naïve Bayes, belief propagation, only test edges, 18 edges per example
Experiment 2
Naïve Bayes, belief propagation, only test edges, 18 edges per example
9/13/05
AIML Seminar

11. Experiment 3
Logistic regression, belief propagation, 18 edges per example
Only train-test edges
Test-test and train-test edges
9/13/05
AIML Seminar

12. Neural Network, voting, only test edges, 18 edges per example
Experiment 4
Neural Network, voting, only test edges, 18 edges per example
9/13/05
AIML Seminar

13. Logistic regression, belief propagation, only test edges
Experiment 5
Logistic regression, belief propagation, only test edges
9/13/05
AIML Seminar