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Showing 20 of 1,217 academic articles
• 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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cc-by-nc-nd
DOI: https://doi.org/10.7551/mitpress/8291.003.0006
Sridhar Chandrasekaran, Yao-Feng Chang, Firman Mangasa Simanjuntak • Nanomaterials
(2026)
The memristor has long been known as a nonvolatile memory technology alternative and has recently been explored for neuromorphic computing, owing to its capability to mimic the synaptic plasticity of the human brain. The architecture of a m...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/nano16030179
Eleftheria Polychronidou, Ilias Kalamaras, Andreas Agathangelidis, Lesley-Ann Sutton et al. • BMC Bioinformatics
(2018)
Abstract Background Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D stru...
DOAJ
Open Access (DOAJ)
DOI: 10.1186/s12859-018-2381-1
Azim Keshavarzian Nodehi, Nasrollah Moghadam Charkari • Machine Learning with Applications
(2022)
Word Sense Disambiguation (WSD) is one of the earliest problems in natural language processing which aims to determine the correct sense of words in context. The semantic information provided by WSD systems is highly beneficial to many task...
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.mlwa.2022.100369
Jiaoling Qin, Jiaoling Qin, Sunzhe Li, Sunzhe Li et al. • Frontiers in Marine Science
(2023)
Accurately monitoring and predicting the large-scale dynamic changes of water levels in coastal zones is essential for its protection, restoration and sustainable development. However, there has been a challenge for achieving this goal usin...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fmars.2022.1113387
Matthias S. Treder • Frontiers in Neuroscience
(2020)
MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers and regression models, using modern optimization algorithms. High-level functions allow for the multivaria...
DOAJ
Open Access (DOAJ)
DOI: 10.3389/fnins.2020.00289
David John Lary, Amir H. Alavi, Amir H. Gandomi, Annette L. Walker • Geoscience Frontiers
(2015)
Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine...
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cc-by-nc-nd
DOI: https://doi.org/10.1016/j.gsf.2015.07.003
Dana Pessach, Erez Shmueli • ACM Computing Surveys
(2022)
An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college ad...
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DOI: https://doi.org/10.1145/3494672
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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cc-by-nc-nd
DOI: https://doi.org/10.7551/mitpress/10290.001.0001
Qifang Bi, Katherine E Goodman, Joshua Kaminsky, Justin Lessler • American Journal of Epidemiology
(2019)
Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on "Big Data," it offers epidemiologists new tools to tackle problems for which classical methods are not well...
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DOI: https://doi.org/10.1093/aje/kwz189
Tim Mueller, Aaron Gilad Kusne, Rampi Ramprasad • Reviews in computational chemistry
(2016)
This chapter addresses the role that data-driven approaches, especially machine learning methods, are expected to play in materials research in the immediate future. Machine learning, an important part of artificial intelligence, has made m...
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DOI: https://doi.org/10.1002/9781119148739.ch4
Silvânia Alves Braga de Castro, André Carlos Silva • REM: International Engineering Journal
(2024)
Abstract The modeling of mineral deposits has been improved over the years with the incorporation of mineralogical and metallurgical information obtained from drilling samples that make up the pillars for the construction of resource models...
DOAJ
Open Access (DOAJ)
DOI: 10.1590/0370-44672023770071
Tao Song, Jiarong Wang, Danya Xu, Wei Wei et al. • Journal of Marine Science and Engineering
(2021)
Physical oceanography models rely heavily on grid discretization. It is known that unstructured grids perform well in dealing with boundary fitting problems in complex nearshore regions. However, it is time-consuming to find a set of unstru...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/jmse9121398
Hilal M. El Misilmani, Tarek Naous • International Conference on High Performance Computing & Simulation (HPCS)
(2019)
With the growth and wide variety of available data, advanced processing, and affordable data storage, machine learning is witnessing great attention in finding optimized solutions in various fields. Machine learning techniques are currently...
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DOI: https://doi.org/10.1109/hpcs48598.2019.9188224
El‐Ghazali Talbi • ACM Computing Surveys
(2021)
During the past few years, research in applying machine learning (ML) to design efficient, effective, and robust metaheuristics has become increasingly popular. Many of those machine learning-supported metaheuristics have generated high-qua...
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Open Access
DOI: https://doi.org/10.1145/3459664
Rahul C. Deo • Circulation
(2015)
Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular va...
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Open Access
DOI: https://doi.org/10.1161/circulationaha.115.001593
PMC: 5831252
Ling Huang, Anthony D. Joseph, Blaine A. Nelson, Benjamin I. P. Rubinstein et al. • ACM Workshop on Artificial Intelligence and Security (AISec)
(2011)
In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a...
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DOI: https://doi.org/10.1145/2046684.2046692
Adi Laurentiu Tarca, Vincent J. Carey, Xuewen Chen, Roberto J. Romero et al. • PLoS Computational Biology
(2007)
The term machine learning refers to a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. Two facets of mechan...
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public-domain
DOI: https://doi.org/10.1371/journal.pcbi.0030116
PMC: 1904382
Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich et al. • eNeuro
(2020)
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve deco...
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cc-by
DOI: https://doi.org/10.1523/eneuro.0506-19.2020
PMC: 7470933
Rafael A. Irizarry • Chapman & Hall/CRC eBooks
(2019)
The machine learning approach is to train an algorithm using a dataset for which the people do know the outcome, and then apply this algorithm in the future to make a prediction when they don't know the outcome. In this chapter, the authors...
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DOI: https://doi.org/10.1201/9780429341830-27