Tree Learning: Optimal Algorithms and Sample Complexity - Hierarchical Clustering and PAC Learning

Tree Learning: Optimal Algorithms and Sample Complexity - Hierarchical Clustering and PAC Learning

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Non-realizable case

17 of 19

17 of 19

Non-realizable case

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Tree Learning: Optimal Algorithms and Sample Complexity - Hierarchical Clustering and PAC Learning

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  1. 1 Intro
  2. 2 Hierarchical clustering (HC)
  3. 3 Practical HC algorithms
  4. 4 Application: phylogenetic tree
  5. 5 Almost correct tree
  6. 6 Our settings
  7. 7 Classification in HC settings
  8. 8 PAC Learning: Algorithm
  9. 9 PAC Learning: Sample Complexity
  10. 10 Naive generalization of VC dimension
  11. 11 Natarajan dimension for HC: Lower Bound
  12. 12 Tree Building
  13. 13 Choosing contradictory constraints
  14. 14 Proof Outline
  15. 15 Non-binary trees
  16. 16 k-tuples
  17. 17 Non-realizable case
  18. 18 Online settings
  19. 19 Conclusion

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