V-shaped interval insensitive loss for ordinal classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00243387" target="_blank" >RIV/68407700:21230/16:00243387 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/franc/Antoniuk-VILMA-ML2016-submission.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/franc/Antoniuk-VILMA-ML2016-submission.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10994-015-5541-9" target="_blank" >10.1007/s10994-015-5541-9</a>
Alternative languages
Result language
angličtina
Original language name
V-shaped interval insensitive loss for ordinal classification
Original language description
We address a problem of learning ordinal classifiers from partially annotated examples. We introduce a V-shaped interval-insensitive loss function to measure discrepancy between predictions of an ordinal classifier and a partial annotation provided in the form of intervals of candidate labels. We show that under reasonable assumptions on the annotation process the Bayes risk of the ordinal classifier can be bounded by the expectation of an associated interval-insensitive loss. We propose several convex surrogates of the interval-insensitive loss which are used to formulate convex learning problems. We described a variant of the cutting plane method which can solve large instances of the learning problems. Experiments on a real-life application of human age estimation show that the ordinal classifier learned from cheap partially annotated examples can achieve accuracy matching the results of the so-far used supervised methods which require expensive precisely annotated examples.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
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)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Machine Learning
ISSN
0885-6125
e-ISSN
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Volume of the periodical
103
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
23
Pages from-to
261-283
UT code for WoS article
000374683800006
EID of the result in the Scopus database
2-s2.0-84953378956