Results 11 to 20 of about 1,371,617 (304)
Aerospace, 2022 The rapid growth of global aviation operations has made its negative environmental impact an international concern. Accurate modeling of aircraft fuel burn, emissions, and noise is the prerequisite for informing new operational procedures, technologies ...
Zhenyu Gao, D. Mavris
semanticscholar +1 more source
Machine Learning in Official Statistics
CoRR, 2018 In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a "Proof of Concept Machine Learning" as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and international statistical institutions and ...
Martin Beck +2 more
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Gaussian Processes for Machine Learning [PDF]
, 2005 A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.
Rasmussen, Carl Edward +1 more
core +1 more source
Theory and Novel Applications of Machine Learning [PDF]
, 2009 Even since computers were invented, many researchers have been trying to understand how human beings learn and many interesting paradigms and approaches towards emulating human learning abilities have been proposed.
core +2 more sources
Proximal Algorithms in Statistics and Machine Learning
Statistical Science, 2015 In this paper we develop proximal methods for statistical learning. Proximal point algorithms are useful in statistics and machine learning for obtaining optimization solutions for composite functions. Our approach exploits closed-form solutions of proximal operators and envelope representations based on the Moreau, Forward-Backward, Douglas-Rachford ...
Polson, Nicholas G. +2 more
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When and How to Apply Statistics, Machine Learning and Deep Learning Techniques
International Conference on Transparent Optical Networks, 2018 Machine Learning has become 'commodity' in engineering and experimental sciences, as calculus and statistics did before. After the hype produced during the 00's, machine learning (statistical learning, neural networks, etc.) has become a solid and ...
Josep Lluis Berral-Garcia
semanticscholar +1 more source
Remote Sensing, 2021 Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu ...
Jun Liu +6 more
semanticscholar +1 more source
Statistical Machine Learning for Human Behaviour Analysis [PDF]
Entropy, 2020 Human behaviour analysis has introduced several challenges in various fields, such as applied information theory, affective computing, robotics, biometrics and pattern recognition [...]
Thomas B. Moeslund +4 more
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Machine Learning Models for Statistical Analysis
The International Arab Journal of Information Technology, 2023 Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant ...
Marko Grebovic +4 more
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Machine learning renormalization group for statistical physics
Machine Learning: Science and Technology, 2023 Abstract We develop a machine-learning renormalization group (MLRG) algorithm to explore and analyze many-body lattice models in statistical physics. Using the representation learning capability of generative modeling, MLRG automatically learns the optimal renormalization group (RG) transformations from self-generated spin configurations
Wanda Hou, Yi-Zhuang You
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