As one of the important research topics in machine learning, loss function plays an important
role in the construction of machine learning algorithms and the improvement of their …

Abstract Machine learning (ML) is continuously unleashing its power in a wide range of
applications. It has been pushed to the forefront in recent years partly owing to the advent of …

The loss function, also known as cost function, is used for training a neural network or other
machine learning models. Over the past decade, researchers have designed many loss …

The classification problem is closely related to the clustering problem discussed in Chaps. 6
and 7. While the clustering problem is that of determining similar groups of data points, the …

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 …

Quantum Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …

Learning a classifier from positive and unlabeled data is an important class of classification
problems that are conceivable in many practical applications. In this paper, we first show that …

We discuss binary classification from only from positive and unlabeled data (PU
classification), which is conceivable in various real-world machine learning problems. Since …

We review some of the literature on semi-supervised learning in this paper. Traditional
classifiers need labeled data (feature/label pairs) to train. Labeled instances however are …

Abstract Energy-Based Models (EBMs) capture dependencies between variables by
associating a scalar energy to each configuration of the variables. Inference consists in …