In this section we present our minimax formulation for linear decision boundaries. Let x and y denote random vectors in a binary classification problem, with ...

Abstract. When constructing a classifier, the probability of correct classifi(cid:173) cation of future data points should be maximized. In the current paper ...

Nov 20, 2019 · Abstract:Deep neural networks enjoy a powerful representation and have proven effective in a number of applications.

We construct a distribution-free Bayes optimal classifier called the Minimum Error Minimax Proba- bility Machine (MEMPM) in a worst-case setting, i.e., ...

Mar 15, 2021 · The Minimax Probability Machine (MPM) method is a competitive algorithm for binary classification problems which was first proposed in [29]. It ...

Minimax Probability Machine (MPM) is a binary classifier that optimizes the upper bound of the misclassification probability. This upper bound of the ...

Sep 2, 2021 · On the other hand, the minimax probability machine is a popular method for binary classification problems and aims at learning a linear ...

Minimax probability machine (MPM) is an excellent discriminant classifier based on prior knowledge. It can directly estimate a probability accuracy bound by ...

Abstract—This paper investigates the multi-class Minimax. Probability Machine (MPM). MPM constructs a binary classifier that provides a worst-case bound on ...

The purpose of this paper is to develop a new minimax probability machine for the $F_\beta$ measure, called MPMF, which can be used to deal with imbalanced ...