Results 21 to 30 of about 9,750,100 (244)
Exploring and Exploiting Conditioning of Reinforcement Learning Agents
IEEE Access, 2020 The outcome of Jacobian singular values regularization was studied for supervised learning problems. In supervised learning settings for linear and nonlinear networks, Jacobian regularization allows for faster learning.
Arip Asadulaev +3 more
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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
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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
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Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets
Complexity, 2021 K-nearest neighbours (kNN) is a very popular instance-based classifier due to its simplicity and good empirical performance. However, large-scale datasets are a big problem for building fast and compact neighbourhood-based classifiers. This work presents
Stanislav Protasov, Adil Mehmood Khan
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Explainable AI: A Review of Machine Learning Interpretability Methods
Entropy, 2020 Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks.
Pantelis Linardatos +2 more
semanticscholar +1 more source
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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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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Supervised Learning in Physical Networks: From Machine Learning to Learning Machines [PDF]
Physical Review X, 2021 18 pages, 9 ...
Menachem Stern +3 more
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Physical Review D, 2022 We propose a simple method to identify a continuous Lie algebra symmetry in a dataset through regression by an artificial neural network. Our proposal takes advantage of the $ \mathcal{O}(ε^2)$ scaling of the output variable under infinitesimal symmetry transformations on the input variables. As symmetry transformations are generated post-training, the
Sean Craven +3 more
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Thumbs up? Sentiment Classification using Machine Learning Techniques [PDF]
Conference on Empirical Methods in Natural Language Processing, 2002 We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform ...
B. Pang +2 more
semanticscholar +1 more source