Learning data discretization via convex optimization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00301606" target="_blank" >RIV/68407700:21230/16:00301606 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/vojtech/Vojtech-TR-2016-01.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/vojtech/Vojtech-TR-2016-01.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Learning data discretization via convex optimization
Original language description
Discretization of continuous input functions into piecewise constant or piecewise linear approximations is needed in many mathematical modeling problems. It has been shown that choosing the length of the piecewise segments adaptively based on data samples leads to improved accuracy of the subsequent processing such as classification. Traditional approaches are often tied to a particular classification model which results in local greedy optimization of a criterion function. This paper proposes a technique for learning the discretization parameters along with the parameters of a decision function in a convex optimization of the true objective. The general formulation is applicable to a wide range of learning problems. Empirical evaluation demonstrates that the proposed convex algorithms yield models with fewer number of parameters with comparable or better accuracy than the existing methods.
Czech name
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Czech description
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Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Number of pages
19
Place of publication
Praha
Publisher/client name
Center for Machine Perception, K13133 FEE Czech Technical University
Version
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