Computation time as a function of the sample size on the random graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

F1 score of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

Subjects:

Examples of ground-truth graph structures with (<i>p</i>, <i>n</i>_≠0) = (10, 10). by Tomokaze Shiratori (9635271)

Subjects:

Number of edges selected through cross-validation on the chain graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Number of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Graphs of the regularization terms. by Tomokaze Shiratori (9635271)

Subjects:

F1 score of a given number of selected edges on the chain graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Computation time as a function of the number of variables on the chain graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Computation time as a function of the number of variables on the random graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

F1 score of a given number of selected edges on the random graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

F1 score of edges selected through cross-validation on the random graph dataset. by Tomokaze Shiratori (9635271)

Subjects:

Reshaping Industrial Maintenance with Machine Learning: Fouling Control Using Optimized Gaussian Process Regression by Francesco Negri (12992994)

Supplementary file 1_Machine learning algorithms to predict epidural-related maternal fever: a retrospective study.docx by Xiaohui Guo (131711)

Vibration Nondestructive Testing of Continuous Welded Rails: A Finite Element Analysis by Alireza Enshaeian (20360253)

One case of log file anomaly detection is shown. by Hao Zhang (15339)

Proposed hybrid GA- ML algorithms. by Ankur Kumar (1357440)

“…This study uses machine learning (ML) algorithms and feature selection based on GA to improve the accuracy and efficiency of diagnosing thyroid disorders using the UCI thyroid dataset. …”

Proposed GA-ML algorithms pseudocode. by Ankur Kumar (1357440)

“…This study uses machine learning (ML) algorithms and feature selection based on GA to improve the accuracy and efficiency of diagnosing thyroid disorders using the UCI thyroid dataset. …”

The comparison of results from the proposed architecture and state-of-the-art algorithms on the UNSW-B15, NSL-KDD and KDDCUP’99 dataset. by Abdullah Asım Yılmaz (20715311)

Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment by Eli Ben-Michael (10803815)

“…We examine a particular case of algorithmic pre-trial risk assessments in the US criminal justice system, which provide deterministic classification scores and recommendations to help judges make release decisions. …”