Learnability of solutions to conjunctive queries
- Submitting institution
-
Birkbeck College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 148
- Type
- D - Journal article
- DOI
-
-
- Title of journal
- Journal of Machine Learning Research
- Article number
- -
- First page
- 1
- Volume
- 20
- Issue
- 67
- ISSN
- 1532-4435
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- URL
-
http://jmlr.org/papers/v20/17-734.html
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
3 - Knowledge Representation and Data Management
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article completes a classification programme that was open for numerous years, deepening and expanding upon techniques introduced by Bova, Chen and Valeriote 2013, and thus strengthening their potential use by future works.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -