Pages that link to "Item:Q1917100"
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The following pages link to Lower bounds on learning decision lists and trees (Q1917100):
Displaying 22 items.
- The ordered covering problem (Q722532) (← links)
- Learning optimal decision trees using constraint programming (Q823772) (← links)
- Self-improved gaps almost everywhere for the agnostic approximation of monomials (Q884469) (← links)
- Measuring teachability using variants of the teaching dimension (Q924171) (← links)
- Lower bounds on probabilistic linear decision trees (Q1067786) (← links)
- Rank-\(r\) decision trees are a subclass of \(r\)-decision lists (Q1198056) (← links)
- Decision lists over regular patterns. (Q1874229) (← links)
- On domain-partitioning induction criteria: worst-case bounds for the worst-case based (Q1885908) (← links)
- Multi-label feature ranking with ensemble methods (Q2217404) (← links)
- Approximating optimal binary decision trees (Q2428690) (← links)
- Minimization of decision trees is hard to approximate (Q2475411) (← links)
- PAC learning under helpful distributions (Q2771492) (← links)
- A syntactic characterization of bounded-rank decision trees in terms of decision lists (Q4349779) (← links)
- Learning Optimal Decision Sets and Lists with SAT (Q5026234) (← links)
- On the hardness of approximating the minimum consistent OBDD problem (Q5054808) (← links)
- Decision List Compression by Mild Random Restrictions (Q5056434) (← links)
- (Q5092472) (← links)
- Monotone term decision lists (Q5941293) (← links)
- Decision lists and related Boolean functions (Q5958318) (← links)
- SAT-based optimal classification trees for non-binary data (Q6049430) (← links)
- Properly learning decision trees in almost polynomial time (Q6551255) (← links)
- Constant depth formula and partial function versions of MCSP are hard (Q6654556) (← links)
