The confidence change of the binary image after iteration. by Dingming Yang (8505990)

Summary of classification algorithms. by Stephen R. Piccolo (8367780)

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Flat optimization method. by Nisha Ann Viswan (20371285)

Classification algorithm hyperparameter combinations. by Stephen R. Piccolo (8367780)

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Summary of feature-selection algorithms. by Stephen R. Piccolo (8367780)

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A Practical Algorithm to Solve the Near-Congruence Problem for Rigid Molecules and Clusters by José Manuel Vásquez-Pérez (12843737)

“…The Fortran implementation of the algorithm is available as an open source library (https://github.com/qcuaeh/molalignlib) and is suitable to be used in global optimization methods for the identification of local minima or basins.…”

Feature-selection algorithm hyperparameter combinations. by Stephen R. Piccolo (8367780)

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Comparison of ranks for classification algorithms across performance metrics. by Stephen R. Piccolo (8367780)

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Face recognition algorithm flow based on the PCA. by Na Lin (69470)

Tradeoff between execution time and predictive performance for classification algorithms. by Stephen R. Piccolo (8367780)

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Relative classification performance per combination of feature-selection and classification algorithm. by Stephen R. Piccolo (8367780)

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Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

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Top 50 genes according to average rank across feature-selection algorithms for GSE10320 and GSE46691. by Stephen R. Piccolo (8367780)

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Relative predictive performance when using default algorithm hyperparameters and all features vs. tuning hyperparameters or selecting features. by Stephen R. Piccolo (8367780)

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Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE46691. by Stephen R. Piccolo (8367780)

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Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE10320. by Stephen R. Piccolo (8367780)

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RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms by Yidan Tang (6623693)

“…The Rosetta automated Monte Carlo reaction-based ligand design (RosettaAMRLD) integrates a Monte Carlo Metropolis (MCM) algorithm and reaction-driven molecule proposal to enhance structure-based <i>de novo</i> drug discovery. …”

Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing hyperparameter optimization. by Stephen R. Piccolo (8367780)

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Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection with hyperparameter optimization. by Stephen R. Piccolo (8367780)

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Space transition methods. by Muhammad Usman Tariq (11022141)