Learning Sets of Rules

This talk is based on

Tom M. Mitchell. Machine Learning. McGraw Hill. 1997. Chapter 10.
and his slides.

1 Learning Rules
2 Rules
3 Sequential Covering
1. 3.1 Learn-One-Rule
  1. 3.1.1 Learn-One-Rule Algorithm
  2. 3.1.2 Learn-One-Rule Example *****
  3. 3.1.3 Learn One Summary
2. 3.2 CN2
3. 3.3 Other Variations
4. 3.4 Other Performance Measures
4 Learning Rule Sets Summary
5 Learning Rule Sets Summary
6 Learning First-Order Rules
1. 6.1 First-Order Logic Definitions
2. 6.2 Learning First-Order Horn Clauses
3. 6.3 FOIL
4. 6.4 Generating Candidate Specializations
5. 6.5 FOIL Example ******
6. 6.6 Guiding Search in FOIL
7. 6.7 Foil-Gain
8. 6.8 Learning Recursive Rule Sets
9. 6.9 FOIL Summary
7 Induction As Inverted Deduction
1. 7.1 Inverted Example
2. 7.2 Induction and Deduction
3. 7.3 Inverse Entailment
4. 7.4 Inverse Entailment Pros and Cons
5. 7.5 Resolution Rule
6. 7.6 Inverting Resolution
7. 7.7 Learning With Inverted Resolution
8. 7.8 First-Order Resolution
9. 7.9 Inverting First-Order Resolution
10. 7.10 Inverted First-Order Example
11. 7.11 Inverse Resolution Summary
8 Generalization, θ-Subsumption, and Entailment
9 PROGOL

All in one page
Index on left