Interval Insensitive Loss for Ordinal Classification

Result code in IS VaVaI
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Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Interval Insensitive Loss for Ordinal Classification
Original language description
We address a problem of learning ordinal classifier from partially annotated examples. We introduce an interval-insensitive loss function to measure discrepancy between predictions of an ordinal classifier and a partial annotation provided in the form ofintervals of admissible labels. The proposed interval-insensitive loss is an instance of loss functions previously used for learning of different classification models from partially annotated examples. We propose several convex surrogates of the interval-insensitive loss which can be efficiently optimized by existing solvers. Experiments on standard benchmarks and a real-life application show that ordinal classifiers learned from partially annotated examples can achieve accuracy close to the accuracyof classifiers learned from completely annotated examples.
Czech name
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Czech description
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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)

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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