… Regularized minimax probability machine for classification In this section, a regularized
formulation is proposed for extending the MPM and MEMPM approaches. Following the …

… does not include a regularization term for the … of minimax probability machine, we propose
a novel binary classification method called regularized twin minimax probability machine …

… In order to incorporate the manifold regularization framework, we introduce a manifold
regularization term to the objective in the optimization problem (4), as in the case of Belkin et al. (…

… Tsang, and JM Zurada, “A class of singleclass minimax probability machines for novelty …
Yang, “Structural regularized support vector machine: A framework for structural large margin …

… The regularised ellipsoids we consider in the high-probability setting are given by … In this
paper we addressed an oversight of the original minimax probability machine (Lanckriet et al. …

… propose a minimax probability extreme learning machine framework (MPME), which combines
the benefits of minimax probability machine (MPM) with extreme learning machine (ELM). …

… To overcome this disadvantage, this paper proposes a novel method for multiclass dimension
reduction, named dimension reduction by minimum error minimax probability machine (DR…

… Regularized twin minimax probability machine regression In this section, we will detail the
proposed regression method, alled regularized twin minimax probability machine regression …

… a minimax interpretation of the maximum likelihood problem over generalized linear models
as well as some popular regularization … of central interest in machine learning. Given the …

… minimax probability machine regression, BMPLS builds a regression model by maximizing
the worst probability … applies regularization theory to BMPLS and propose regularized BMPLS …