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