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Statistics versus machine learning [PDF]
Nature Methods, 2018 Two major goals in the study of biological systems are inference andprediction. Inference creates a mathematical model of the datageneration process to formalize our understanding or test ahypothesis about how the system behaves. Prediction aims atforecasting unobserved outcomes or future behavior, such as whethera mouse with a given phenotype will ...
Bzdok, Danilo +2 more
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Spatial Statistics & Machine Learning.
Proposed for presentation at the Sandia Machine Learning/Deep Learning Workshop held July 25-28, 2022 in ,, 2022 Lyndsay Shand
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Technology- enabled advance in the worlds of statistics, machine learning and data mining
New Zealand Science Review, 2023 Advances in digital computing continue to have large effects on all aspects of life and society, including science. These advances are possible because we have computer languages that translate directly into computational steps that can be implemented ...
J. Maindonald
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Representativeness in Statistics, Politics, and Machine Learning [PDF]
Conference on Fairness, Accountability and Transparency, 2021 Representativeness is a foundational yet slippery concept. Though familiar at first blush, it lacks a single precise meaning. Instead, meanings range from typical or characteristic, to a proportionate match between sample and population, to a more ...
Kyla Chasalow, K. Levy
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Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities [PDF]
Chemical Engineering and Science, 2021 Recent advances in machine learning, coupled with low-cost computation, availability of cheap streaming sensors, data storage and cloud technologies, has led to widespread multi-disciplinary research activity with significant interest and investment from
Indranil Pan, L. Mason, Omar Matar
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Fighting Money Laundering With Statistics and Machine Learning [PDF]
IEEE Access, 2022 Money laundering is a profound global problem. Nonetheless, there is little scientific literature on statistical and machine learning methods for anti-money laundering. In this paper, we focus on anti-money laundering in banks and provide an introduction
R. Jensen, Alexandros Iosifidis
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Matrix Factorization Techniques in Machine Learning, Signal Processing, and Statistics
Mathematics, 2023 Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Sparse coding represents a signal as a sparse linear combination of atoms, which are elementary signals derived from a predefined dictionary ...
Ke-Lin Du +3 more
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Predicting the type of auditor opinion: Statistics, machine learning, or a combination of the two?
European Journal of Applied Economics, 2019 The goal of this study is to overcome the identified methodological limitations of prior studies aimed at predicting the type of auditor opinion and draw definite conclusions on the relative predictive performance of different predictive methods for this
Nemanja Stanišić +2 more
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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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Diagnostics, 2022 The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine ...
S. K. Dhillon +4 more
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