Classification is a fundamental task in science and engineering on which machine learning
methods have shown outstanding performances. However, it is challenging to determine …

Contemporary machine learning applications often involve classification tasks with many
classes. Despite their extensive use, a precise understanding of the statistical properties and …

In classification problems, as the number of classes increases, correctly classifying a new
instance into one of them is assumed to be more challenging than making the same …

The growing literature on" benign overfitting" in overparameterized models has been mostly
restricted to regression or binary classification settings; however, most success stories of …

In this paper, we investigate the generalization performance of multi-class classification, for
which we obtain a shaper error bound by using the notion of local Rademacher complexity …

This paper studies the generalization performance of multi-class classification algorithms, for
which we obtain, for the first time, a data-dependent generalization error bound with a …

Semisupervised classification (SSC) algorithms use labeled and unlabeled data to predict
labels of unseen instances. Classifier ensembles have been successfully studied and …

This paper suggests a method for multiclass learning with many classes by simultaneously
learning shared characteristics common to the classes, and predictors for the classes in …

We investigate the problem of multiclass classification with rejection, where a classifier can
choose not to make a prediction to avoid critical misclassification. First, we consider an …

We study consistency properties of surrogate loss functions for general multiclass
classification problems, defined by a general loss matrix. We extend the notion of …