Classification-based Probabilistic Modeling of Texture Transition for Fast Line Search Tracking and Delineation

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Monitoring outdoor urban parking areas has traditionally relied on either costly sensing technology, such as ground sensors, or manual inspections, which are both resource-intensive. The emergence of drones equipped with advanced visual sensors provide a c ...

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Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

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In this manuscript we consider the problem of kernel classification. While worst-case bounds on the decay rate of the prediction error with the number of samples are known for some classifiers, they often fail to accurately describe the learning curves of ...

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An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are consi ...

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There has been a recent surge of interest in the study of asymptotic reconstruction performance in various cases of generalized linear estimation problems in the teacher-student setting, especially for the case of i.i.d standard normal matrices. Here, we g ...

Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

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In high dimension, low sample size (HDLSS) settings, classifiers based on Euclidean distances like the nearest neighbor classifier and the average distance classifier perform quite poorly if differences between locations of the underlying populations get m ...

Classification-based Probabilistic Modeling of Texture Transition for Fast Line Search Tracking and Delineation

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Probabilistic methods for neural combinatorial optimization

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Error scaling laws for kernel classification under source and capacity conditions

Bayes-optimal Learning of Deep Random Networks of Extensive-width

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Robustness and invariance properties of image classifiers

On Generalizations of Some Distance Based Classifiers for HDLSS Data