Learning Logically Defined Hypotheses - Martin Grohe, RWTH Aachen University

Learning Logically Defined Hypotheses - Martin Grohe, RWTH Aachen University

Alan Turing Institute via YouTube Direct link

Example 1 (cont d)

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5 of 21

Example 1 (cont d)

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Learning Logically Defined Hypotheses - Martin Grohe, RWTH Aachen University

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  1. 1 Intro
  2. 2 Outline
  3. 3 Declarative ML
  4. 4 Idea of Model-Theoretic Framework
  5. 5 Example 1 (cont d)
  6. 6 Example 2
  7. 7 Formal Framework For simplicity, we only consider Boolean classification problems
  8. 8 Learning as Minimisation
  9. 9 Remarks on VC-Dimension and PAC-Learning
  10. 10 Computation Model
  11. 11 Complexity Considerations
  12. 12 Proof
  13. 13 Strings as Background Structures
  14. 14 Learning with Local Access
  15. 15 Monadic Second-Order Logic
  16. 16 Building an Index
  17. 17 Factorisation Trees as Index Data Structures
  18. 18 Learning MSO
  19. 19 Pre-Processing
  20. 20 Learning Phase 1
  21. 21 Open Problems

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