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 …

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 …

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 …

We compare classification and regression tasks in an overparameterized linear model with
Gaussian features. On the one hand, we show that with sufficient overparameterization all …

Most literature on support vector machines (SVMs) concentrates on the dual optimization
problem. In this chapter, we would like to point out that the primal problem can also be …

We briefly describe the main ideas of statistical learning theory, support vector machines
(SVMs), and kernel feature spaces. We place particular emphasis on a description of the so …

Theory of Classification: a Survey of Some Recent Advances Page 1 ESAIM: PS ESAIM:
Probability and Statistics November 2005, Vol. 9, p. 323–375 DOI: 10.1051/ps:2005018 …

We consider the problem of learning a binary classifier from a training set of positive and
unlabeled examples, both in the inductive and in the transductive setting. This problem …

This draft textbook is extracted from lecture notes from a class that I have taught
(unfortunately online, but this gave me an opportunity to write more detailed notes) during …

The purpose of this paper is to investigate statistical properties of risk minimization based
multicategory classification methods. These methods can be considered as natural …