Results 11 to 20 of about 10,169,362 (317)

Federated learning based on stratified sampling and regularization

open access: yesComplex & Intelligent Systems, 2022
Federated learning (FL) is a new distributed learning framework that is different from traditional distributed machine learning: (1) differences in communication, computing, and storage performance among devices (device heterogeneity), (2) differences in
Chenyang Lu   +4 more
doaj   +2 more sources

Efficient Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling. [PDF]

open access: yesJ R Stat Soc Series B Stat Methodol, 2022
In many contemporary applications, large amounts of unlabelled data are readily available while labelled examples are limited. There has been substantial interest in semi‐supervised learning (SSL) which aims to leverage unlabelled data to improve ...
Gronsbell J, Liu M, Tian L, Cai T.
europepmc   +2 more sources

Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling [PDF]

open access: yesnpj Computational Materials, 2023
Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond that accessible by ab initio methods and play an increasingly important role in the study and design of materials.
Ji Qi   +4 more
semanticscholar   +1 more source

Generalized Stratified Sampling for Efficient Reliability Assessment of Structures Against Natural Hazards [PDF]

open access: yesJournal of engineering mechanics, 2023
Performance-based engineering for natural hazards facilitates the design and appraisal of structures with rigorous evaluation of their uncertain structural behavior under potentially extreme stochastic loads expressed in terms of failure probabilities ...
S. Arunachalam, S. Spence
semanticscholar   +1 more source

Stratified Sampling-Based Deep Learning Approach to Increase Prediction Accuracy of Unbalanced Dataset

open access: yesElectronics, 2023
Due to the imbalanced nature of datasets, classifying unbalanced data classes and drawing accurate predictions is still a challenging task. Sampling procedures, along with machine learning and deep learning algorithms, are a boon for solving this kind of
Jeyabharathy Sadaiyandi   +3 more
semanticscholar   +1 more source

OPTIMASI ALGORITMA C4.5 MENGGUNAKAN METODE FORWARD SELECTION DAN STRATIFIED SAMPLING UNTUK PREDIKSI KELAYAKAN KREDIT

open access: yesJSiI (Jurnal Sistem Informasi), 2022
Kredit merupakan dana yang diberikan oleh bank kepada pihak lain berdasarkan perjanjian pinjam-meminjam, yang mewajibkan peminjam melunasi pinjamannya setelah jangka waktu tertentu.
Ibnu Ubaedi, Yan Mitha Djaksana
semanticscholar   +1 more source

Estimation of Coefficient of Variation Using Calibrated Estimators in Double Stratified Random Sampling

open access: yesMathematics, 2023
One of the most useful indicators of relative dispersion is the coefficient of variation. The characteristics of the coefficient of variation have contributed to its widespread use in most scientific and academic disciplines, with real life applications.
U. Shahzad   +5 more
semanticscholar   +1 more source

Soft, Sweet, and Colorful: Stratified Sampling Reveals Sequence of Events at the Onset of Grape Ripening

open access: yesAmerican Journal of Enology and Viticulture, 2021
Asynchronous development of grape berries leads to high variation among berry samples collected during veraison. We applied a stratified sampling method that groups berries by firmness to the touch and visible skin color to study the sequence of physical
E. Hernández-Montes   +4 more
semanticscholar   +1 more source

Comparison of quota sampling and stratified random sampling

open access: yesBiometrics & Biostatistics International Journal, 2021
The possibility that researchers should be able to obtain data from all cases is questionable. There is a need; therefore, this article provides a probability and non-probability sampling.
Rufai Iliyasu, I. Etikan
semanticscholar   +1 more source

Using Stratified Sampling to Improve LIME Image Explanations [PDF]

open access: yesAAAI Conference on Artificial Intelligence
We investigate the use of a stratified sampling approach for LIME Image, a popular model-agnostic explainable AI method for computer vision tasks, in order to reduce the artifacts generated by typical Monte Carlo sampling.
Muhammad Rashid   +3 more
semanticscholar   +1 more source

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