Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>14−<i>f</i>23. by Ying Li (38224)

“…<p>Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>14−<i>f</i>23.…”

Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>8−<i>f</i>13. by Ying Li (38224)

“…<p>Convergence curves of the IMGO algorithm and comparison algorithms on functions <i>f</i>8−<i>f</i>13.…”

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

AIF and VOF derived features used to feed the machine learning algorithms. by Ezequiel de la Rosa (11361108)

Critical values of membership functions learned from the study cohort by the algorithm. by Yufeng Zhang (195768)

Great machine learning performance with multiple classification algorithms. by Sydney C. Weiser (15383531)

Convergence curves of the IMGO and comparison algorithms on functions <i>f</i>1−<i>f</i>7. by Ying Li (38224)

“…<p>Convergence curves of the IMGO and comparison algorithms on functions <i>f</i>1−<i>f</i>7.</p>…”

Table_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.DOCX by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

Subjects:

Data_Sheet_1_Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models.PDF by Shakiru A. Alaka (9302864)

“…We evaluate the predictive accuracy of machine-learning algorithms for predicting functional outcomes in acute ischemic stroke patients after endovascular treatment.…”

Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

Recursive attention function and loss function change. by Wang Linfeng (17102068)

Test results of the benchmark function, the dimension fixed to one hundred. by Rahila Hafiz (18137254)

Summary of results of naïve Bayes algorithms. by Christopher E. Niemczak (8586861)

Subjects:

Algorithms for Sparse Support Vector Machines by Alfonso Landeros (798926)

“…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_k</i> where at most <i>k</i> components of <math><mi>β</mi></math> are nonzero. …”

MSVM-RFE algorithm for screening key genes. by Ya xi Zhu (11624730)

Data_Sheet_1_Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations.CSV by Denise Reimann (7213364)

Pipeline of the interactor algorithm. by Jose Cleydson F. Silva (8685594)

Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation. by Changze Ou (22806374)

Depiction of the theory, can sequence infer function? by Ashley I. Heinson (8124374)