Continuation Methods for Approximate Large Scale Object Sequencing
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 84467053
- Type
- D - Journal article
- DOI
-
10.1007/s10994-018-5764-7
- Title of journal
- Machine Learning
- Article number
- -
- First page
- 595
- Volume
- 108
- Issue
- 4
- ISSN
- 0885-6125
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- URL
-
-
- 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
-
3
- Research group(s)
-
A - Computer Science
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Addressing the scalability issue of the 50-year-old seriation problem. First to propose a new set of algorithms capable of large-scale operation whilst maintaining high accuracy.
Downloaded >2,000 times within a year (Springer official numbers).
Resulted in a continuing work in Pattern Recognition (doi.org/10.1016/j.patcog.2019.107192, acceptance rate 19%).
The first author (PGR Evangelopoulos) obtained a postdoc job in the Leverhulme Research Centre for Functional Materials Design on the basis of this work and its extension."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
