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

Subjects:

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

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

Subjects:

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

Machine learning algorithm parameters. by Namal Rathnayake (15361124)

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

Subjects: “…applying independent algorithms…”

Activation functions: Sigmoid function and ReLU function. by Wan-Hua Zhang (21601139)

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

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

“…We derive two closely related algorithms to carry out this strategy. Our simulated and real examples vividly demonstrate how the algorithms achieve better sparsity without loss of classification power. …”

Flowchart of the proposed algorithm. by Heewon Chung (2881286)

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

Subjects:

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

Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning by Bernhard Vennemann (7434353)

The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties by Tijana Šušteršič (14519970)

Subjects:

Concept of the Loss function in the case of Supervised Machine Learning and Physics Machine Learning approach. by Akshansh Mishra (21001499)

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

Classification performance of four machine learning algorithms on the test set. by Maryam Ahmadzadeh (8590380)

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

Ranking machine learning algorithms based on AUC. by F. William Townes (3216846)

“…<p>Numeric values indicate the fraction of times the row algorithm has higher classification performance than the column algorithm. pglm: elastic net penalized logistic regression, svm: support vector machine with radial basis function, xgb: gradient boosted trees, nb: naive Bayes, knn: k-nearest neighbors.…”

Optimization algorithms performance comparison across benchmark functions. by Sundreen Asad Kamal (20390818)