The spatial vector organization schematic. by Haiyan Zhang (176919)

The Lift curve with Support Vector Machine model. by Xuefei Lin (12219118)

Subjects:

Aeronautical imaging model with soft margin support vector machine. by Dongchen Dai (13822355)

Feature selection using Boruta algorithm. by Shayla Naznin (13014015)

“…</p><p>Methods</p><p>Multiple machine learning (ML) algorithms were applied to data from the 2022 Bangladesh Demographic Health Survey, including Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, Support Vector Machine, XGBoost, LightGBM and Neural Networks. …”

The comparison of different ML algorithms on EN dataset in group AB.s. by Hakimeh Khojasteh (16748091)

The 452 registrations of the 750 feature vectors after dimension reduction using the AI algorithm autoencoder, the 6 clusters obtained with the DBSCAN clustering algorithm are colo... by Gerhard Fritsch (405224)

Deep Spectral Clustering (DSC) Algorithm: A convolutional autoencoder (CAE) is trained on input image stack (<i>S</i>) to extract latent feature vectors (<i>LFV</i>). by Creighton M. Lisowski (22756444)

Boxplots of the ARI and log<i>L</i>(Θ) for the emEM, RndEM and the proposed algorithm MRIPEM. by Jie You (42305)

Boxplots of the ARI and log<i>L</i>(Θ) for the emEM, RndEM and the proposed algorithm MRIPEM. by Jie You (42305)

Boxplots of the ARI and log<i>L</i>(Θ) for the emEM, RndEM and the proposed algorithm MRIPEM. by Jie You (42305)

Boxplots of the ARI and log<i>L</i>(Θ) for the emEM, RndEM and the proposed algorithm MRIPEM. by Jie You (42305)

List of relevant studies using DL and ML algorithms for skin cancer identification. by Ahmad Naeem (15246749)

Bash script for downloading records using Feature Generator API. by Paul J. Albert (10515299)

The algorithm for the Transformer-DN model. by Liang Na (22226855)

Table_2_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

Table_1_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

Table_3_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

Table_6_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

Table_5_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”

Table_4_Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies.XLSX by Ming Li (91180)

“…We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. …”