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Optimized ensemble machine learning model for cyberattack classification in industrial IoT. [PDF]

Front Artif Intell
Alabdullah B, Sankaranarayanan S.
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Machine learning models based wear performance prediction of AZ31/TiC composites. [PDF]

Sci Rep
Kumar TS, Raghu R, Petrů J, Shalini S, Kirubavathi G, Kalita K.   +5 more
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Insights into long-acting reversible contraceptive practices in Sub-Saharan Africa: A machine learning perspective. [PDF]

PLoS One
Mengistu AK, Getinet K, Tizie SB, Messelu MA, Anteneh A, Alemayehu MA, Gedefaw AE.   +6 more
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Binary classification of signal and background triggers of a transition edge sensor using convolutional neural networks. [PDF]

Sci Rep
Rivasto E, Isleif KS, Januschek F, Lindner A, Meyer M, Othman G, Rubiera Gimeno JA, Schwemmbauer C.   +7 more
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Population-Based Hyperparameter Tuning With Multitask Collaboration

IEEE Transactions on Neural Networks and Learning Systems, 2023
Population-based optimization methods are widely used for hyperparameter (HP) tuning for a given specific task. In this work, we propose the population-based hyperparameter tuning with multitask collaboration (PHTMC), which is a general multitask collaborative framework with parallel and sequential phases for population-based HP tuning methods.
Wendi Li, Ting Wang, Wing W. Y. Ng
openaire   +2 more sources

Kriging Hyperparameter Tuning Strategies

AIAA Journal, 2008
Response surfaces have been extensively used as a method of building effective surrogate models of high-fidelity computational simulations. Of the numerous types of response surface models, kriging is perhaps one of the most effective, due to its ability to model complicated responses through interpolation or regression of known data while providing an
Toal, David J.J., Bressloff, Neil W., Keane, Andy J.   +2 more
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Convolutional Neural Networks Hyperparameters Tuning

2021
Digital images have revolutionized work in numerous scientific fields such as healthcare, astronomy, biology, agriculture as well as in every day life. One of the frequent tasks in applications with digital images is image classification which is a very challenging task. Major progress was made when convolution neural networks were introduced.
Eva Tuba, Nebojša Bačanin, Ivana Strumberger, Milan Tuba   +3 more
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Beyond Manual Tuning of Hyperparameters

KI - Künstliche Intelligenz, 2015
The success of hand-crafted machine learning systems in many applications raises the question of making machine learning algorithms more autonomous, i.e., to reduce the requirement of expert input to a minimum. We discuss two strategies towards this goal: (1) automated optimization of hyperparameters (including mechanisms for feature selection ...
Hutter, Frank, Lücke, Jörg, Schmidt-Thieme, Lars   +2 more
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Sequential Model-Free Hyperparameter Tuning

2015 IEEE International Conference on Data Mining, 2015
Hyperparameter tuning is often done manually but current research has proven that automatic tuning yields effective hyperparameter configurations even faster and does not require any expertise. To further improve the search, recent publications propose transferring knowledge from previous experiments to new experiments.
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme   +2 more
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