Machine Learning: Generative and Discriminative Models
Machine Learning: Generative and Discriminative Models
Sargur N. Srihari srihari@cedar.buffalo.edu
Machine Learning Course:
Machine Learning
Srihari
Outline of Presentation
1. What is Machine Learning? ML applications, ML as Search
2. Generative and Discriminative Taxonomy 3. Generative-Discriminative Pairs
Classifiers: Na?ve Bayes and Logistic Regression Sequential Data: HMMs and CRFs 4. Performance Comparison in Sequential Applications NLP: Table extraction, POS tagging, Shallow parsing, Handwritten word recognition, Document analysis 5. Advantages, disadvantages 6. Summary 7. References
2
Machine Learning
Srihari
1. Machine Learning
? Programming computers to use example data or past experience
? Well-Posed Learning Problems
? A computer program is said to learn from experience E
? with respect to class of tasks T and performance measure P,
? if its performance at tasks T, as measured by P, improves with experience E.
3
Machine Learning
Srihari
Problems Too Difficult To Program by Hand
? Learning to drive an autonomous vehicle
? Train computer-controlled vehicles to steer correctly
? Drive at 70 mph for 90 miles on public highways
? Associate steering commands with image sequences
Task T: driving on public, 4-lane highway using vision sensors Perform measure P: average distance traveled before error
(as judged by human overseer) Training E: sequence of images and steering commands recorde4 d
while observing a human driver
Machine Learning
Srihari
Example Problem:
Handwritten Digit Recognition
Wide variability of same numeral
? Handcrafted rules will result in large no of rules and exceptions
? Better to have a machine that learns from a large training set
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