Search alternatives:
bayesian optimization » based optimization (Expand Search)
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
mapk bayesian » task bayesian (Expand Search), a bayesian (Expand Search), art bayesian (Expand Search)
data derived » data driven (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
bayesian optimization » based optimization (Expand Search)
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
mapk bayesian » task bayesian (Expand Search), a bayesian (Expand Search), art bayesian (Expand Search)
data derived » data driven (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
binary mapk » binary mask (Expand Search), binary image (Expand Search)
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Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment
Published 2019“…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”
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Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods
Published 2022“…Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package <i>miselect</i>. …”
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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.…”