Due to the increasing popularity of support vector machine (SVM) and the introduction of
Universum, many variants of SVM along with Universum such as Universum support vector …

Conformal prediction uses the degree of strangeness (nonconformity) of data instances to
determine the confidence values of new predictions. We propose an inductive conformal …

Ordinal classification is a problem setting in-between nominal classification and metric
regression, where the goal is to predict classes of an ordinal scale. Usually, there is a clear …

As an effective data augmentation method, Mixup synthesizes an extra amount of samples
through linear interpolations. Despite its theoretical dependency on data properties, Mixup …

Distance metric learning has motivated a great deal of research over the last years due to its
robustness for many pattern recognition problems. In this paper, we develop a supervised …

In open-set recognition (OSR), a promising strategy is exploiting pseudo-unknown data
outside given K known classes as an additional K+ 1-th class to explicitly model potential …

Metric learning, which aims to determine an appropriate distance function to measure the
similarity and dissimilarity between data points accurately, is one of the most popular …

Imbalanced data poses a substantial challenge to conventional classification methods,
which often disproportionately favor samples from the majority class. To mitigate this issue …

Figuring out students' willingness to participate in interactive teaching activities is conductive
to promoting the application of distance education, thereby assisting teachers to better …

Conformal prediction uses the degree of strangeness (nonconformity) of new data instances
to determine the confidence values of new predictions. Conformal predictors are …