Add to ORKGInternational audienceIn this paper, a proposition is made to learn the parameters of evidential contextual correction mechanisms from a learning set composed of soft labelled data, that is data where the true class of each object is only partially known. The method consists in optimizing a measure of discrepancy between the values of the corrected contour function and the ground truth also represented by a contour function. The advantages of this method are illustrated by tests on synthetic and real data
International audienceActive learning is a subfield of machine learning which allows to reduce the a...
International audienceLearning from class-imbalanced datasets has gained substantial attention in th...
International audienceThe theory of belief functions has been successfully used in many classificati...
International audienceIn this paper, a proposition is made to learn the parameters of evidential con...
International audienceIn this paper, we propose to learn the parameters of evidential contextual cor...
International audiencePartially supervised learning extends both supervised and unsu-pervised learni...
International audienceKnowledge about the quality of a source can take several forms: it may for ins...
International audienceThe evidential K nearest neighbor classifier is based on discounting evidence ...
International audienceClassification is used to predict classes by extracting information from label...
International audienceThe Evidential K-Nearest-Neighbor (EK-NN) method provided a global treatment o...
In machine learning problems, the availability of several classifiers trained on different data or f...
We address the problem of uncertainty quantification in the domain of face attribute classification,...
International audienceIn this paper, we investigate ways to learn efficiently from uncertain data us...
International audienceThe class imbalance issue involves many real world domains such as fraud detec...
International audienceActive learning is a subfield of machine learning which allows to reduce the a...
International audienceLearning from class-imbalanced datasets has gained substantial attention in th...
International audienceThe theory of belief functions has been successfully used in many classificati...
International audienceIn this paper, a proposition is made to learn the parameters of evidential con...
International audienceIn this paper, we propose to learn the parameters of evidential contextual cor...
International audiencePartially supervised learning extends both supervised and unsu-pervised learni...
International audienceKnowledge about the quality of a source can take several forms: it may for ins...
International audienceThe evidential K nearest neighbor classifier is based on discounting evidence ...
International audienceClassification is used to predict classes by extracting information from label...
International audienceThe Evidential K-Nearest-Neighbor (EK-NN) method provided a global treatment o...
In machine learning problems, the availability of several classifiers trained on different data or f...
We address the problem of uncertainty quantification in the domain of face attribute classification,...
International audienceIn this paper, we investigate ways to learn efficiently from uncertain data us...
International audienceThe class imbalance issue involves many real world domains such as fraud detec...
International audienceActive learning is a subfield of machine learning which allows to reduce the a...
International audienceLearning from class-imbalanced datasets has gained substantial attention in th...
International audienceThe theory of belief functions has been successfully used in many classificati...