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

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

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

“…Variable selection reveals the crucial features, reduces the dimensionality of feature space, and improves model interpretation. In the support vector machine literature, variable selection is achieved by <math><mrow><msub><mrow><mi>l</mi></mrow><mn>1</mn></msub></mrow></math> penalties. …”

The structure of the proposed algorithm. by Mingbao Yang (14008799)

Model selection for machine learning algorithm based on microbial abundances. by Elio L. Herzog (11576876)

Subjects: “…applying independent algorithms…”

Functions of a translation model. by Beibei Ren (9662634)

Calculation results of commonly used machine learning algorithm models. by Qianhui He (13522729)

The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. by Ahmed M. Elshewey (21463867)

Damping oscillation function (before adding random numbers). by Fujun Wang (96184)

Damping oscillation function (after adding random numbers). by Fujun Wang (96184)

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

Modeling effect of different automl algorithms. by Guangyuan Ge (11520226)

Model construction and validation (A) Construction of the model based on multiple machine learning algorithms. by Chengting Wu (21485612)

Comparison of key evaluation indicators for recommendation algorithms. by Mingbao Yang (14008799)

Parameters of the test function. by Yiqiong Gao (16698968)

“…The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. …”

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.…”

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

Machine learning algorithm parameters. by Namal Rathnayake (15361124)

“…Results of the study showcased that both systems can simulate river flows as a function of catchment rainfalls; however, the Cat gradient Boosting algorithm (CatBoost) has a computational edge over the Adaptive Network Based Fuzzy Inference System (ANFIS). …”

Benchmark functions. by Mingjun Li (178925)

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.…”