Results 21 to 30 of about 1,259,133 (314)
F1000Research, 2022 Science is justly admired as a cumulative process (“standing on the shoulders of giants”), yet scientific knowledge is typically built on a patchwork of research contributions without much coordination.
Judith ter Schure, Peter Grünwald
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Journal of Physics A: Mathematical and Theoretical, 2020 Abstract 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,
Owen Howell +3 more
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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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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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Introduction to Machine Learning [PDF]
, 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 and processing power of computers. Together with many other disciplines, machine learning methods have been widely
Baştanlar, Yalın, Özuysal, Mustafa
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Structured Prediction - Beyond Support Vector Machine and Cross Entropy [PDF]
, 2021 Presented online via Bluejeans Events on September 29, 2021 at 12:15 p.m.Francis Bach is a researcher at INRIA in the Computer Science department of Ecole Normale Supérieure, in Paris, France.
Bach, Francis
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Quantum-chemical insights from deep tensor neural networks
Nature Communications, 2017 Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical ...
Kristof T. Schütt +4 more
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Distributional Prototypical Methods for Reliable Explanation Space Construction
IEEE Access, 2023 As deep learning has been successfully deployed in diverse applications, there is an ever increasing need to explain its decision. To explain decisions, case-based reasoning has proved to be effective in many areas.
Hyungjun Joo +3 more
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Learning curves for decision making in supervised machine learning: a survey [PDF]
Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e.g., the number of training examples or the number of ...
van Rijn J.N., Mohr F.
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Power Allocation Schemes Based on Deep Learning for Distributed Antenna Systems
IEEE Access, 2020 In recent years, a lot of power allocation algorithms have been proposed to maximize spectral efficiency (SE) and energy efficiency (EE) for the distributed antenna systems (DAS).
Gongbin Qian +4 more
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