An expectation-maximization algorithm for finding noninvadable stationary states. by Robert Marsland (8616483)

“…<i>(b)</i> Metabolic byproducts move the relevant unperturbed state from <b>R</b>⁰ (gray ‘x’) to (black ‘x’), which is itself a function of the current environmental conditions. …”

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

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ROC analysis of the Machine Learning Algorithm. by Stephan Ellmann (314565)

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

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

Rule-in and rule-out criteria provided by the machine learning algorithm. by Stephan Ellmann (314565)

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Unimodal benchmark functions. by Rahila Hafiz (18137254)

“…We propose a fusion of this algorithm with a discrete recombinant evolutionary strategy to enhance initialization diversity. …”

Multimodal benchmark functions. by Rahila Hafiz (18137254)

“…We propose a fusion of this algorithm with a discrete recombinant evolutionary strategy to enhance initialization diversity. …”

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.…”

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

An Efficient Algorithm for Minimizing Multi Non-Smooth Component Functions by Minh Pham (2458399)

“…<p>Many problems in statistics and machine learning can be formulated as an optimization problem of a finite sum of nonsmooth convex functions. …”

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.…”

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

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

“…A Gaussian mutation strategy was integrated with IFPA to optimise the initial input weights and thresholds of the extreme learning machine (ELM), improve the balance and exploration ability of the algorithm, and increase the efficiency and accuracy for identifying pipeline defects. …”

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

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). …”

Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

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

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Details of S-shaped and V-shaped functions. by Yan Wei (32168)

“…Therefore, this study proposed a feature selection prediction model (bGEBA-SVM) based on an improved bat algorithm and support vector machine by extracting 1694 college graduates from 2022 classes in Zhejiang Province. …”

Algorithm process of BPNN. by Xiaoxiang Cao (12644851)