MORD: Multi-class Classifier for Ordinal Regression
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00211706" target="_blank" >RIV/68407700:21230/13:00211706 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/antoniuk/Antoniuk-Franc-Hlavac-ECML-2013.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/antoniuk/Antoniuk-Franc-Hlavac-ECML-2013.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-40994-3_7" target="_blank" >10.1007/978-3-642-40994-3_7</a>
Alternative languages
Result language
angličtina
Original language name
MORD: Multi-class Classifier for Ordinal Regression
Original language description
We show that classification rules used in ordinal regression are equivalent to a certain class of linear multi-class classifiers. This observation not only allows to design new learning algorithms for ordinal regression using existing methods for multi-class classification but it also allows to derive new models for ordinal regression. For example, one can convert learning of ordinal classifier with (almost) arbitrary loss function to a convex unconstrained risk minimization problem for which many efficient solvers exist. The established equivalence also allows to increase discriminative power of the ordinal classifier without need to use kernels by introducing a piece-wise ordinal classifier. We demonstrate advantages of the proposed models on standard benchmarks as well as in solving a real-life problem. In particular, we show that the proposed piece-wise ordinal classifier applied to visual age estimation outperforms other standard prediction models.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Machine Learning and Knowledge Discovery in Databases
ISBN
978-3-642-40993-6
ISSN
0302-9743
e-ISSN
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Number of pages
16
Pages from-to
96-111
Publisher name
Springer-Verlag, GmbH
Place of publication
Heidelberg
Event location
Prague
Event date
Sep 23, 2013
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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