Results 11 to 20 of about 394,431 (266)
Learning multiple defaults for machine learning algorithms [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021 The performance of modern machine learning methods highly depends on their hyperparameter configurations. One simple way of selecting a configuration is to use default settings, often proposed along with the publication and implementation of a new algorithm.
Pfisterer, F. +4 more
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Joint Learning of Generative Translator and Classifier for Visually Similar Classes
IEEE Access, 2020 In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings where classes are visually similar and data is scarce. For this purpose, we propose joint learning from a scratch to
Byungin Yoo +3 more
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Absent Multiple Kernel Learning Algorithms [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Multiple kernel learning (MKL) has been intensively studied during the past decade. It optimally combines the multiple channels of each sample to improve classification performance. However, existing MKL algorithms cannot effectively handle the situation where some channels of the samples are missing, which is not uncommon in practical applications ...
Xinwang Liu +8 more
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Basis exchange and learning algorithms for extracting collinear patterns
Journal of Information and Telecommunication, 2021 Understanding large data sets is one of the most important and challenging problems in the modern days. Exploration of genetic data sets composed of high dimensional feature vectors can be treated as a leading example in this context.
Leon Bobrowski, Paweł Zabielski
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Instance-Based Learning Algorithms [PDF]
Machine Learning, 1991 Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks.
Aha, David W. +2 more
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Collaborative Learning Platform Using Learning Optimized Algorithms [PDF]
, 2021 Aware that the lack of mathematical knowledge and skills is a major problem for the development of a modern, inclusive and informed society, the MathE partnership has developed a tool that is aimed at bridging the gap that moves students away from courses that rely on a mathematical core.
Azevedo, Beatriz Flamia +5 more
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Learning dynamic algorithm portfolios [PDF]
Annals of Mathematics and Artificial Intelligence, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gagliolo, Matteo, Schmidhuber, Juergen
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Decoding the grasping intention from electromyography during reaching motions
Journal of NeuroEngineering and Rehabilitation, 2018 Background Active upper-limb prostheses are used to restore important hand functionalities, such as grasping. In conventional approaches, a pattern recognition system is trained over a number of static grasping gestures. However, training a classifier in
Iason Batzianoulis +4 more
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The dropout learning algorithm [PDF]
Artificial Intelligence, 2014 Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable ...
Baldi, Pierre, Sadowski, Peter
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Hydrogen, 2023 A proton exchange membrane (PEM) electrolyzer is fed with water and powered by electric power to electrochemically produce hydrogen at low operating temperatures and emits oxygen as a by-product.
Mohammad Biswas +2 more
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