Physics Reports. — 2019. — Vol. 810. — P. 1-124. Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and...
IEEE Transactions on Neural Networks, Vol. 10, No. 5, Sept 1999. — p. 988-999. This article presents a very general overview of statistical learning theory including both theoretical and algorithmic aspects of the theory. The goal of this overview is to demonstrate how the abstract learning theory established conditions for generalization which are more general than those...
Reviews of Modern Physics. — 2019. — Vol. 91. — Art. number 045002 (p. 1-39). Machine learning (ML) encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. This article reviews in a selective way the recent research on the interface between machine learning and the...
Article. - SCIENCE. - 17 NOVEMBER 2006 - P. 1118-1121 Animals sustain the ability to operate after injury by creating qualitatively different compensatory behaviors. Although such robustness would be desirable in engineered systems, most machines fail in the face of unexpected damage. We describe a robot that can recover from such change autonomously, through continuous...
Springer, 2021. — 725 p. — (Lecture Notes in Electrical Engineering 778). — ISBN: 978-981-16-3066-8. Covers research in the areas of artificial intelligence, machine learning, and deep learning applications. The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning -...
Physical Review E83, 016107, 21/01/2011. - 11 p. Stochastic block models have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world. networks, which typically display broad degree...
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