Research map / Learning theory and algorithms / Sample complexity
Tree Learning: Optimal Sample Complexity and Algorithms
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the realizable rather than agnostic PAC setting | Hierarchical tree learning on labeled k-tuples · Labeled k-tuples, PAC | sample complexity: Θ( n / ϵ2 · polylog( 1 / ϵ , 1 / δ )) -> Θ( n / ϵ · polylog( 1 / ϵ , 1 / δ )) | Helped |
| Increase the number of labeled triplets | Top-down tree construction · Binary realizable, random trees and ImageNet hierarchy | prediction error | Helped |
| Increase the number of labeled triplets | Top-down tree construction · Agnostic, Spambase | prediction error | Helped |
| Increase the number of labeled triplets | Top-down tree construction · Agnostic, random 100-dimensional vectors | prediction error | No clear effect |
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