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Hinge Loss for Any Classifier! We can swap cross-entropy for hinge loss anytime
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Classroom Contents
CMU Neural Nets for NLP - Structured Prediction Basics
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- 1 A Prediction Problem
- 2 Types of Prediction
- 3 Why Call it "Structured" Prediction?
- 4 Many Varieties of Structured Prediction!
- 5 Sequence Labeling w
- 6 Why Model Interactions in Output? . Consistency is important!
- 7 A Tagger Considering Output Structure movie
- 8 Training Structured Models
- 9 Local Normalization and
- 10 The Structured Perceptron Algorithm . An extremely simple way of training (non-probabilistic) global models . Find the one-best, and it's score is better than the correct answer adjust parameters to …
- 11 Contrasting Perceptron and Global Normalization
- 12 Structured Training and Pre-training
- 13 Hinge Loss for Any Classifier! We can swap cross-entropy for hinge loss anytime
- 14 Cost-augmented Hinge
- 15 Costs over Sequences
- 16 Cost-Augmented Decoding for Hamming Loss
- 17 Solution 1: Sample Mistakes in Training (Ross et al. 2010)
