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Basic Classification in ML. !!!!$$$!!!!. . . . Spam filtering. Characterrecognition. . Input . Output . C. [thanks to Ben Taskar for slide!]. Binary. Multi-Class. Structured Classification. . Handwritingrecognition. . . . Input . Output . 3D objectrecognition. building. . tree. . brace. Structured output.

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## Multi-Class and Structured Classification

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**1. **Multi-Class and Structured Classification Guillaume Obozinski
Practical Machine Learning CS 294
Tuesday 5/06/08

**2. **Basic Classification in ML

**3. **Structured Classification

**4. **Multi-Class Classification Multi-class classification : direct approaches
Nearest Neighbor
Generative approach & Naďve Bayes
Linear classification:
geometry
Perceptron
K-class (polychotomous) logistic regression
K-class SVM
Multi-class classification through binary classification
One-vs-All and All-vs-all
Calibration
Precision-Recall curve

**5. **Multi-label classification

**6. **Nearest Neighbor, Decision Trees

**7. **Generative models

**8. **Generative models

**9. **Naďve Bayes

**10. **Discriminative linear classification

**11. **Geometry of Linear classification

**12. **Three discriminative algorithms

**13. **Multiclass Perceptron

**14. **Polychotomous logistic regression

**15. **Multi-class SVM

**16. **Real world classification problems

**17. **Combining binary classifiers

**18. **Calibration Want universal confidence score for different methods
Want universal confidence score for different methods

**19. **Calibration

**20. **Calibration

**21. **Combining OVA calibrated classifiers

**22. **Confusion Matrix

**23. **Precision & Recall

**24. **Precision-Recall

**25. **Precision Recall Curve

**26. **Structured classification

**27. **Local Classification
Classify using local information
? Ignores correlations!

**28. **Structured Classification Use local information
Exploit correlations

**29. **Local Classification Lets look at the output.
Look at majority of the pixels. Roughly correct. In fact, around 70% accuracy.
However, there are some serious problemsLets look at the output.
Look at majority of the pixels. Roughly correct. In fact, around 70% accuracy.
However, there are some serious problems

**30. **Structured Classification

**31. **Structured Classification Structured models
Examples of structures
Scoring parts of the structure
Probabilistic models and linear classification
Learning algorithms:
Generative approach: (Bayesian modeling with graphical models)
Linear classification:
Structured Perceptron
Conditional Random Fields (counterpart of logistic regression)
Large-margin structured classification

**32. **Structured classification:

**33. **Tree model 1

**34. **Tree model 1 Haplotype = genotype + order of strandsHaplotype = genotype + order of strands

**35. **Tree Model 2:Hierarchical Text Classification

**36. **Grid model

**37. **Cliques and Features

**39. **Exponential form

**40. **Decoding and Learning

**41. **Decoding and Learning

**42. **Our favorite (discriminative) algorithms

**43. **(Averaged) Perceptron

**44. **Example: multi-class setting

**45. **CRF

**46. **Summary For multi-class classification
Combine multiple binary classifiers
Logistic regression produces calibrated values
One-vs-all or All-vs-all (both fast)
For structured classification
Define a structured score for which efficient dynamic program exist
Simple start with structured perceptron
For better performance use CRF or Max-margin methods (M3-net, SVMstruct)

**47. **Object Segmentation Results

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