Results 11 to 20 of about 3,002,307 (310)
Machine learning and deep learning [PDF]
Electronic Markets, 2021 AbstractToday, 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 analytical model building and solve associated tasks.
Christian Janiesch +2 more
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Quantum machine learning [PDF]
Nature, 2017 Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently produced by classical systems, it is reasonable to postulate that quantum computers may outperform classical computers
Seth Lloyd +6 more
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Entropy, 2023 We explore unique considerations involved in fitting machine learning (ML) models to data with very high precision, as is often required for science applications. We empirically compare various function approximation methods and study how they scale with increasing parameters and data.
Eric J. Michaud, Ziming Liu, Max Tegmark
openaire +5 more sources
Data‐driven performance metrics for neural network learning
International Journal of Adaptive Control and Signal Processing, EarlyView., 2023 Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri +2 more
wiley +1 more source
Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint
Arthritis Research & Therapy, 2022 Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has ...
Alix Bird +8 more
doaj +1 more source
Machine Learning in the Air [PDF]
IEEE Journal on Selected Areas in Communications, 2019 Thanks to the recent advances in processing speed and data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story -- ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles.
D. Gunduz +5 more
openaire +4 more sources
Machine Learning for Software Engineering: Models, Methods, and Applications [PDF]
, 2018 Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data. Machine learning has been successfully applied in many areas of software engineering ranging from behaviour extraction, to testing, to bug fixing ...
Bennaceur, Amel, Meinke, Karl
core +1 more source
Machine learning-based prediction of a BOS reactor performance from operating parameters [PDF]
, 2020 A machine learning-based analysis was applied to process data obtained from a Basic Oxygen Steelmaking (BOS) pilot plant. The first purpose was to identify correlations between operating parameters and reactor performance, defined as rate of ...
Li, Zushu +2 more
core +1 more source
Machine learning as ecology [PDF]
Journal of Physics A: Mathematical and Theoretical, 2020 Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms - including Support Vector Machines (SVMs) -- have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark ...
Owen Howell +4 more
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Machine learning and essentialism
Philosophical Problems in Science (Zagadnienia Filozoficzne w Nauce), 2022 Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but ...
Kristina Šekrst, Sandro Skansi
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