Search alternatives:
bayesian optimization » based optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
mask bayesian » task bayesian (Expand Search), a bayesian (Expand Search), art bayesian (Expand Search)
binary small » primary small (Expand Search), binary image (Expand Search)
small model » small bowel (Expand Search)
binary mask » binary image (Expand Search)
bayesian optimization » based optimization (Expand Search)
model optimization » codon optimization (Expand Search), global optimization (Expand Search), based optimization (Expand Search)
mask bayesian » task bayesian (Expand Search), a bayesian (Expand Search), art bayesian (Expand Search)
binary small » primary small (Expand Search), binary image (Expand Search)
small model » small bowel (Expand Search)
binary mask » binary image (Expand Search)
-
1
-
2
-
3
-
4
The comparison of the accuracy score of the benchmark and the proposed models.
Published 2025Subjects: -
5
-
6
-
7
Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).
Published 2025Subjects: -
8
-
9
Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction.
Published 2025Subjects: -
10
ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.
Published 2025Subjects: -
11
The statistical description of the original data set of the patients (<i>n</i> = 162).
Published 2025Subjects: -
12
-
13
The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
Published 2025Subjects: -
14
-
15
DataSheet_1_Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates.pdf
Published 2022“…Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.…”
-
16
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.…”