Turney, Peter D. (1994) A theory of cross-validation error. [Journal (Paginated)]
Full text available as:
PDF
161Kb |
Abstract
This paper presents a theory of error in cross-validation testing of algorithms for predicting real-valued attributes. The theory justifies the claim that predicting real-valued attributes requires balancing the conflicting demands of simplicity and accuracy. Furthermore, the theory indicates precisely how these conflicting demands must be balanced, in order to minimize cross-validation error. A general theory is presented, then it is developed in detail for linear regression and instance-based learning.
Item Type: | Journal (Paginated) |
---|---|
Subjects: | Computer Science > Artificial Intelligence Computer Science > Machine Learning Computer Science > Statistical Models |
ID Code: | 1820 |
Deposited By: | Turney, Peter |
Deposited On: | 13 Oct 2001 |
Last Modified: | 11 Mar 2011 08:54 |
References in Article
Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.
Metadata
- ASCII Citation
- Atom
- BibTeX
- Dublin Core
- EP3 XML
- EPrints Application Profile (experimental)
- EndNote
- HTML Citation
- ID Plus Text Citation
- JSON
- METS
- MODS
- MPEG-21 DIDL
- OpenURL ContextObject
- OpenURL ContextObject in Span
- RDF+N-Triples
- RDF+N3
- RDF+XML
- Refer
- Reference Manager
- Search Data Dump
- Simple Metadata
- YAML
Repository Staff Only: item control page