Set-valued classification, a new classification paradigm that aims to
id...
In machine learning, crowdsourcing is an economical way to label a large...
Set classification aims to classify a set of observations as a whole, as...
We consider an additive model with a main effect and effects from multip...
Individualized treatment recommendation (ITR) is an important analytic
f...
Two hitherto disconnected threads of research, diverse exploration (DE) ...
Nearest neighbor is a popular class of classification methods with many
...
Accurate estimation of the run time of computational codes has a number ...
We address the challenge of effective exploration while maintaining good...
Nearest neighbor is a popular nonparametric method for classification an...
The goal of confidence-set learning in the binary classification setting...
In many real applications of statistical learning, a decision made from
...
Classification and clustering are both important topics in statistical
l...
Classification is an important tool with many useful applications. Among...
Ordinal data are often seen in real applications. Regular multicategory
...
The stability of statistical analysis is an important indicator for
repr...
Set classification problems arise when classification tasks are based on...
Classification is an important topic in statistics and machine learning ...
A novel linear classification method that possesses the merits of both t...