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.
|Title of host publication||Algorithmic Learning Theory - 25th International Conference, ALT 2014, Proceedings|
|Editors||Peter Auer, Alexander Clark, Thomas Zeugmann, Sandra Zilles|
|Number of pages||14|
|State||Published - 2014|
|Event||25th International Conference on Algorithmic Learning Theory, ALT 2014 - Bled, Slovenia|
Duration: 8 Oct 2014 → 10 Oct 2014
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||25th International Conference on Algorithmic Learning Theory, ALT 2014|
|Period||8/10/14 → 10/10/14|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)