بدائل البحث:
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
python function » protein function (توسيع البحث)
where function » sphere function (توسيع البحث), gene function (توسيع البحث), wave function (توسيع البحث)
algorithm a » algorithm _ (توسيع البحث), algorithm b (توسيع البحث), algorithms _ (توسيع البحث)
a function » _ function (توسيع البحث)
algorithm python » algorithm within (توسيع البحث), algorithms within (توسيع البحث), algorithm both (توسيع البحث)
python function » protein function (توسيع البحث)
where function » sphere function (توسيع البحث), gene function (توسيع البحث), wave function (توسيع البحث)
algorithm a » algorithm _ (توسيع البحث), algorithm b (توسيع البحث), algorithms _ (توسيع البحث)
a function » _ function (توسيع البحث)
-
101
Optimization outcome for the Levy function.
منشور في 2025"…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …"
-
102
-
103
Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?
منشور في 2020"…In order to examine the true power of machine-learning algorithms in scoring function formulation, we have conducted a systematic study of six off-the-shelf machine-learning algorithms, including Bayesian Ridge Regression (BRR), Decision Tree (DT), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Linear Support Vector Regression (L-SVR), and Random Forest (RF). …"
-
104
-
105
-
106
-
107
As for Fig 2, we present failure rates as a function of the cohort size (vertical axis) versus the number of distractors (horizontal axis), for the Smyth and McClave baseline algorithm from [76].
منشور في 2020"…In the middle left, the converse case, where the embedded cohort is skewed, but the distractors balanced, and finally in the middle right a case where both the embedded cohort to be selected and the distractors have a highly skewed distribution. …"
-
108
-
109
Algorithms for Sparse Support Vector Machines
منشور في 2022"…Penalties still appear, but serve a different purpose. The proximal distance principle takes a loss function <math><mrow><mi>L</mi><mo>(</mo><mi>β</mi><mo>)</mo></mrow></math> and adds the penalty <math><mrow><mi>ρ</mi><mn>2</mn>dist<mrow><mrow><mo>(</mo><mi>β</mi><mo>,</mo><msub><mrow><mi>S</mi></mrow><mi>k</mi></msub><mo>)</mo></mrow></mrow><mn>2</mn></mrow></math> capturing the squared Euclidean distance of the parameter vector <math><mi>β</mi></math> to the sparsity set <i>S<sub>k</sub></i> where at most <i>k</i> components of <math><mi>β</mi></math> are nonzero. …"
-
110
Multidomain, Automated Photopatterning of DNA-functionalized Hydrogels (MAPDH).
منشور في 2024"…<p><b>A)</b> Generalized workflow for fabricating DNA-functionalized hydrogels. …"
-
111
-
112
-
113
A synthetic biology approach for the design of genetic algorithms with bacterial agents
منشور في 2022"…To this end, we designed a genetic algorithm, which we have named BAGA, illustrating its utility solving simple instances of optimization problems such as function optimization, 0/1 knapsack problem, Hamiltonian path problem. …"
-
114
-
115
-
116
-
117
-
118
-
119
-
120