Machine Learning in Computer Vision

1. Introduction

2. Theory: Probabilistic Classifiers

3. Theory: Generalization Bounds

4. Theory: Semi-Supervised Learning

5. Algorithm: Maximum Likelihood Minimum Entropy HMM

6. Algorithm: Margin Distribution Optimization

7. Algorithm: Learning the Structure of Bayesian Network Classifiers

8. Application: Office Activity Recognition

9. Application: Multimodal Event Detection

10. Application: Facial Expression Recognition

11. Application: Bayesian Network Classifiers for Face Detection

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Keywords: COMPUTERS / Computer Science COM014000