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beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data

Abstract : During the past few years, the machine learning community has paid attention to developing new methods for learning from weakly labeled data. This field covers different settings like semi-supervised learning, learning with label proportions, multi-instance learning, noise-tolerant learning, etc. This paper presents a generic framework to deal with these weakly labeled scenarios. We introduce the \betarisk as a generalized formulation of the standard empirical risk based on surrogate margin-based loss functions. This risk allows us to express the reliability on the labels and to derive different kinds of learning algorithms. We specifically focus on SVMs and propose a soft margin \betasvm algorithm which behaves better that the state of the art.
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https://hal.archives-ouvertes.fr/hal-01359298
Contributor : Valentina Zantedeschi <>
Submitted on : Tuesday, November 15, 2016 - 3:57:48 PM
Last modification on : Monday, January 13, 2020 - 5:46:06 PM
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  • HAL Id : hal-01359298, version 1

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Valentina Zantedeschi, Rémi Emonet, Marc Sebban. beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data. NIPS 2016, Dec 2016, Barcelona, Spain. ⟨hal-01359298⟩

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