Discovery and development of biopeptides are time‐consuming, laborious, and dependent
on various factors. Data‐driven computational methods, especially machine learning (ML) …

The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …

Objective Many machine learning models have aided medical specialists in diagnosis and
prognosis for breast cancer. Accuracy has been regarded as a primary measurement for the …

Background Prognostic studies of breast cancer survivability have been aided by machine
learning algorithms, which can predict the survival of a particular patient based on historical …

Introduction Peptidomics is an emerging field of omics sciences using advanced isolation,
analysis, and computational techniques that enable qualitative and quantitative analyses of …

We study a regression problem where for some part of the data we observe both the label
variable (Y) and the predictors (X), while for other part of the data only the predictors are …

Semi-supervised inference: General theory and estimation of means Page 1 The Annals of
Statistics 2019, Vol. 47, No. 5, 2538–2566 https://doi.org/10.1214/18-AOS1756 © Institute of …

We investigate the benefit of combining both cluster assumption and manifold assumption
underlying most of the semi-supervised algorithms using the flexibility and the efficiency of …

This paper explores the field of semi-supervised Fr\'echet regression, driven by the
significant costs associated with obtaining non-Euclidean labels. Methodologically, we …

In this paper, we propose a robust and fast transductive support vector machine (RTSVM)
classifier that can be applied to large-scale data. To this end, we use the robust Ramp loss …