Results 221 to 230 of about 98,022 (269)

Hyperparameter optimization to enhance the performance of deep learning models for the early detection of invasive turtles in Korea. [PDF]

Sci Rep
Baek JW, Kim JI, Mun MH, Kim CB.
europepmc   +1 more source

A practical ML framework for biomass torrefaction analysis and simulator deployment. [PDF]

Sci Rep
Park S, Yang J, Kim S, Oh KC, Hwang K, Lee J, Ryu S.   +6 more
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FalsEye: proactive detection of false data injection attacks in smart grids using IceCube-optimised ensemble learning. [PDF]

Sci Rep
Sheta AN, Osman SF, Eladl AA, Sedhom BE, El-Afifi MI.   +4 more
europepmc   +1 more source

Intelligent incremental classification using a dynamic grasshopper-enhanced neural network for data streams. [PDF]

Sci Rep
Darwish SM, El-Shoafy NA.
europepmc   +1 more source

Enhancing generalizability in classification of peripheral neural recordings with graph neural network. [PDF]

PLoS One
Ji RQ, Dousty M, Koh RGL, Sejdić E.
europepmc   +1 more source

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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
exaly   +3 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
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +2 more sources