6533b822fe1ef96bd127caf8
RESEARCH PRODUCT
Learning from good examples
Rolf WiehagenEfim KinberRusins Freivaldssubject
AlgebraTransduction (machine learning)Inductive transferComputational learning theoryInductive biasbusiness.industryAlgorithmic learning theoryUnsupervised learningMulti-task learningArtificial intelligenceInstance-based learningbusinessMathematicsdescription
The usual information in inductive inference for the purposes of learning an unknown recursive function f is the set of all input /output examples (n,f(n)), n ∈ ℕ. In contrast to this approach we show that it is considerably more powerful to work with finite sets of “good” examples even when these good examples are required to be effectively computable. The influence of the underlying numberings, with respect to which the learning problem has to be solved, to the capabilities of inference from good examples is also investigated. It turns out that nonstandard numberings can be much more powerful than Godel numberings.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 1995-01-01 |