Then, we use Simhash to encode the selected parts of the analysis files to create feature vectors. These vectors are then used to train different Machine Learning algorithms ... showed that using ...

The method leverages pre-trained convolutional neural networks (CNNs) for feature extraction and traditional machine-learning algorithms for classification ... attaining an accuracy of 97.50% in the ...

It is a hot topic how entanglement, a quantity from quantum information theory, can assist machine learning ... We also apply our feature extraction algorithm to the binary MPS classifiers of the ...

The proposed algorithm easily implements cycle-based feature extraction ... of regular classifiers due to the imbalance issue caused by SSO data being substantially less than non-SSO data, a weighted ...

Local binary patterns ... on some datasets. The feature vector can now be processed using the Support vector machine, extreme learning machines, or some other machine-learning algorithm to classify ...

Evaluation metrics, performances for all classifiers are included in the project report. This project is done collaboratively with Nazlıcan Aka and Dağlar Eren Tekşen for CMPE462 - Machine Learning ..

Dr. James McCaffrey from Microsoft Research presents a full-code, step-by-step tutorial on using the LightGBM tree-based system to perform binary classification (predicting a discrete variable that ...

Machine learning ... in which the classifiers were examined. Before evaluating the classifiers independently, the investigators identified reliable metabolites via recursive feature elimination ...