Learning monotone term decision lists
We study the learnability of monotone term decision lists in the exact model of equivalence and membership queries. We show that, for any constant k>0, k-term monotone decision lists are exactly and properly learnable with n^O(k) membership queries in O(n^(k^3)) time. We also show n^Omega(k) memb...
| Autores: | , , |
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| Tipo de documento: | relatório científico |
| Data de publicação: | 1996 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/83105 |
| Acesso em linha: | https://hdl.handle.net/2117/83105 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Learning monotone Decision lists Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica |
| Resumo: | We study the learnability of monotone term decision lists in the exact model of equivalence and membership queries. We show that, for any constant k>0, k-term monotone decision lists are exactly and properly learnable with n^O(k) membership queries in O(n^(k^3)) time. We also show n^Omega(k) membership queries are necessary for exact learning. In contrast, both k-term monotone decision lists (k>1) and general monotone decision lists are not learnable with equivalence queries alone. |
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