Name
Title | ||
|---|---|---|
The Consistency Dimension and Distribution-Dependent Learning from Queries (Extended Abstract) |
Abstract | ||
|---|---|---|
We prove a new combinatorial characterization of polynomial learnability from equivalence queries, and state some of its consequences relating the learnability of a class with the learnability via equivalence and membership queries of its subclasses obtained by restricting the instance space. Then we propose and study two models of query learning in which there is a probability distribution on the instance space, both as an application of the tools developed from the combinatorial characterization and as models of independent interest. |
Year | Venue | Keywords |
|---|---|---|
1999 | ALT | distribution-dependent learning,query learning,new combinatorial characterization,independent interest,extended abstract,polynomial learnability,combinatorial characterization,instance space,consistency dimension,membership query,equivalence query,probability distribution |
Field | DocType | ISBN |
Query learning,Discrete mathematics,Polynomial,Concept class,Computer science,Probability distribution,Equivalence (measure theory),Artificial intelligence,Learnability,Machine learning | Conference | 3-540-66748-2 |
Citations | PageRank | References |
6 | 0.68 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José L. Balcázar | 1 | 701 | 62.06 |
| Jorge Castro | 2 | 18 | 1.80 |
| David Guijarro | 3 | 27 | 2.44 |
| Hans-Ulrich Simon | 4 | 567 | 104.52 |