Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

A new edition of a graduate-level machine learning textbook that focuses on the analysis
and theory of algorithms. This book is a general introduction to machine learning that can …

Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …

Function estimation/approximation is viewed from the perspective of numerical optimization
in function space, rather than parameter space. A connection is made between stagewise …

Boosting is one of the most important recent developments in classification methodology.
Boosting works by sequentially applying a classification algorithm to reweighted versions of …

This paper introduces a general Bayesian framework for obtaining sparse solutions to
regression and classification tasks utilising models linear in the parameters. Although this …

Federated learning (FL) is a distributed deep learning method that enables multiple
participants, such as mobile and IoT devices, to contribute a neural network while their …

Abstract Model selection strategies for machine learning algorithms typically involve the
numerical optimisation of an appropriate model selection criterion, often based on an …

Boosting Boosting—(is an approach to machine learning based on the idea of creating a
highly accurate prediction rule by combining many relatively weak and inaccurate rules. The …