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- W2950766026 abstract "In this paper, we study the problem of learning a monotone DNF with at most $s$ terms of size (number of variables in each term) at most $r$ ($s$ term $r$-MDNF) from membership queries. This problem is equivalent to the problem of learning a general hypergraph using hyperedge-detecting queries, a problem motivated by applications arising in chemical reactions and genome sequencing. We first present new lower bounds for this problem and then present deterministic and randomized adaptive algorithms with query complexities that are almost optimal. All the algorithms we present in this paper run in time linear in the query complexity and the number of variables $n$. In addition, all of the algorithms we present in this paper are asymptotically tight for fixed $r$ and/or $s$." @default.
- W2950766026 created "2019-06-27" @default.
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- W2950766026 date "2014-05-05" @default.
- W2950766026 modified "2023-09-23" @default.
- W2950766026 title "On Exact Learning Monotone DNF from Membership Queries" @default.
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