Learning nearly monotone k-term DNF
This note studies the learnability of the class k-term DNF with a bounded number of negations per term. We study the case of learning with membership queries alone, and give tight upper and lower bounds on the number of negations that makes the learning task feasible. We also prove a negative result...
| Autores: | , , |
|---|---|
| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 1996 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/83098 |
| Acceso en línea: | https://hdl.handle.net/2117/83098 |
| Access Level: | acceso abierto |
| Palabra clave: | DNF Learning monotone Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica |
| Sumario: | This note studies the learnability of the class k-term DNF with a bounded number of negations per term. We study the case of learning with membership queries alone, and give tight upper and lower bounds on the number of negations that makes the learning task feasible. We also prove a negative result for equivalence queries. Finally, we show that a slight modification in our algorithm proves that the considered class is also learnable in the Simple PAC model, extending Li and Vitányi result for monotone k-term DNF. |
|---|