Search alternatives:
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
psc derived » ipsc derived (Expand Search), msc derived (Expand Search), hipsc derived (Expand Search)
binary main » binary mask (Expand Search), binary image (Expand Search), binary pairs (Expand Search)
main model » domain model (Expand Search), chain model (Expand Search), brain model (Expand Search)
derived optimization » driven optimization (Expand Search), required optimization (Expand Search), design optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
psc derived » ipsc derived (Expand Search), msc derived (Expand Search), hipsc derived (Expand Search)
binary main » binary mask (Expand Search), binary image (Expand Search), binary pairs (Expand Search)
main model » domain model (Expand Search), chain model (Expand Search), brain model (Expand Search)
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Thesis-RAMIS-Figs_Slides
Published 2024“…Importantly, this strategy locates samples adaptively on the transition between facies which improves the performance of conventional \emph{<i>MPS</i>} algorithms. In conclusion, this work shows that preferential sampling can contribute in \emph{<i>MPS</i>} even at very small sampling regimes and, as a corollary, demonstrates that prior models (obtained form a training image) can be used effectively not only to simulate non-sensed variables of the field, but to decide where to measure next.…”
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Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat.
Published 2025“…Optimization with GridSearchCV corroborated this stagnation, identifying a simple linear model (C=0.05, gamma='scale') as the optimal configuration, indicating that the additional complexity of nonlinear kernels did not confer predictive gains. …”
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Table_1_Machine Learning Techniques in Blood Pressure Management During the Acute Phase of Ischemic Stroke.DOCX
Published 2022“…Decision trees were constructed by a hierarchical binary recursive partitioning algorithm to predict the BP-lowering of 10–30% off the maximal value when antihypertensive treatment was given in patients with an extremely high BP (above 220/110 or 180/105 mmHg for patients receiving thrombolysis), according to the American Heart Association/American Stroke Association (AHA/ASA), the European Society of Cardiology, and the European Society of Hypertension (ESC/ESH) guidelines. …”