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Showing 20 of 594 academic articles
Niloofar Asefi, Leonard Lupin-Jimenez, Tianning Wu, Ruoying He et al. • Machine Learning. Earth
(2025)
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges fo...
DOAJ
Open Access (DOAJ)
DOI: 10.1088/3049-4753/ae0b70
Dimitrios Kleftogiannnis, Sonia Gavasso, Benedicte Sjo Tislevoll, Nisha van der Meer et al. • iScience
(2024)
Summary: Mass cytometry by time-of-flight (CyTOF) is an emerging technology allowing for in-depth characterization of cellular heterogeneity in cancer and other diseases. Unfortunately, high-dimensional analyses of CyTOF data remain quite d...
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Open Access (DOAJ)
DOI: 10.1016/j.isci.2024.110261
Jörn Lötsch, Alfred Ultsch • Pain
(2017)
1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...
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DOI: https://doi.org/10.1097/j.pain.0000000000001118
PMC: 5895117
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong • Cambridge University Press eBooks
(2020)
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in d...
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DOI: https://doi.org/10.1017/9781108679930
Juliana Aparecida Anochi, Vinícius Albuquerque de Almeida, Haroldo Fraga de Campos Velho • Remote Sensing
(2021)
Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learni...
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Open Access (DOAJ)
DOI: 10.3390/rs13132468
• The MIT Press eBooks
(2012)
Foundations of Machine LearningSoon we will embark on a theoretical study of AdaBoost in order to understand its properties, particularly its ability as a learning algorithm to generalize, that is, to make accurate predictions on data not s...
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DOI: https://doi.org/10.7551/mitpress/8291.003.0006
Joseph A. Cruz, David Scott Wishart • Cancer Informatics
(2006)
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, ...
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DOI: https://doi.org/10.1177/117693510600200030
PMC: 2675494
Oswald Campesato • De Gruyter eBooks
(2020)
This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fun...
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DOI: https://doi.org/10.1515/9781683924937
Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar • The MIT Press eBooks
(2018)
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 serve as a textbook for graduate students and a reference fo...
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DOI: https://doi.org/10.7551/mitpress/10290.001.0001
Marco Barreno, Blaine A. Nelson, Anthony D. Joseph, J. D. Tygar • Machine Learning
(2010)
Machine learning’s ability to rapidly evolve to changing and complex situations has helped it become a fundamental tool for computer security. That adaptability is also a vulnerability: attackers can exploit machine learning systems. We pre...
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DOI: https://doi.org/10.1007/s10994-010-5188-5
Shai Shalev‐Shwartz, Shai Ben-David • Cambridge University Press eBooks
(2014)
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book prov...
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DOI: https://doi.org/10.1017/cbo9781107298019
Lei Zhou, Yuntong Song, Weiqi Ji, Haiqiao Wei • Energy and AI
(2021)
Combustion science is an interdisciplinary study that involves nonlinear physical and chemical phenomena in time and length scales, including complex chemical reactions and fluid flows. Combustion widely supplies energy for powering vehicle...
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DOI: https://doi.org/10.1016/j.egyai.2021.100128
Steven L. Brunton, Bernd R. Noack, Petros Koumoutsakos • Annual Review of Fluid Mechanics
(2019)
The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniq...
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Open Access
DOI: https://doi.org/10.1146/annurev-fluid-010719-060214
JUDE W. SHAVLIK, Thomas E. Deitterich, Thomas G. Dietterich (1991)
From the Publisher:
The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous ...
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Sebastian Raschka (2015)
Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effect...
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Yalın Baştanlar, Mustafa Özuysal • Methods in molecular biology
(2013)
The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity ...
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Open Access
DOI: https://doi.org/10.1007/978-1-62703-748-8_7
Christian Janiesch, Patrick Zschech, Kai Heinrich • Electronic Markets
(2021)
Abstract Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of a...
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DOI: https://doi.org/10.1007/s12525-021-00475-2
Kurt K. Benke, Geza Benke • International Journal of Environmental Research and Public Health
(2018)
Artificial intelligence and automation are topics dominating global discussions on the future of professional employment, societal change, and economic performance. In this paper, we describe fundamental concepts underlying AI and Big Data ...
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DOI: https://doi.org/10.3390/ijerph15122796
PMC: 6313588
Madalina Busuioc • Public Administration Review
(2020)
Artificial intelligence (AI) algorithms govern in subtle yet fundamental ways the way we live and are transforming our societies. The promise of efficient, low-cost, or "neutral" solutions harnessing the potential of big data has led public...
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DOI: https://doi.org/10.1111/puar.13293
PMC: 8518786
Дмитро АНРДЄЄВ, Олексій ЛИГУН, Андрій ДРОЗД, Олена ПОНОЧОВНА • Компютерні системи та інформаційні технології
(2025)
Critical infrastructures are fundamental to the seamless operation of modern societies, encompassing sectors such as energy, healthcare, transportation, and communications. Ensuring their reliability, performance, continuous operation, safe...
DOAJ
Open Access (DOAJ)
DOI: 10.31891/csit-2025-2-3