Search alternatives:
weights optimization » weight optimization (Expand Search), weights initialization (Expand Search), joint optimization (Expand Search)
design optimization » bayesian optimization (Expand Search)
class weights » class weight (Expand Search)
binary task » binary mask (Expand Search)
task design » based design (Expand Search)
weights optimization » weight optimization (Expand Search), weights initialization (Expand Search), joint optimization (Expand Search)
design optimization » bayesian optimization (Expand Search)
class weights » class weight (Expand Search)
binary task » binary mask (Expand Search)
task design » based design (Expand Search)
-
61
Thesis-RAMIS-Figs_Slides
Published 2024“…<br><br>Finally, although the developed concepts, ideas and algorithms have been developed for inverse problems in geostatistics, the results are applicable to a wide range of disciplines where similar sampling problems need to be faced, included but not limited to design of communication networks, optimal integration and communication of swarms of robots and drones, remote sensing.…”
-
62
Data_Sheet_1_A real-time driver fatigue identification method based on GA-GRNN.ZIP
Published 2022“…The specific work is as follows: (1) design simulated driving experiment and real driving experiment, determine the fatigue state of drivers according to the binary Karolinska Sleepiness Scale (KSS), and establish the fatigue driving sample database. (2) Improved Multi-Task Cascaded Convolutional Networks (MTCNN) and applied to face detection. …”
-
63
Data_Sheet_1_Multiclass Classification Based on Combined Motor Imageries.pdf
Published 2020“…In this way, for each binary problem, the CSP algorithm produces features to determine if the specific body part is engaged in the task or not. …”
-
64
Models and Dataset
Published 2025“…The algorithm does not rely on predefined control parameters like crossover or mutation rates, which makes it lightweight and easy to implement for various feature selection and optimization tasks.…”
-
65
Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles
Published 2025“…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”