Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 11 of 11 for “"Boosted decision trees"”.
-
Characteristic Classification of Walkers via Underfloor Accelerometer Gait Measurements through Machine Learning
… machine learning algorithms included are Bagged Decision Trees, Boosted Decision Trees, Support Vector Machines (SVMs), and Neural Networks. Data reduction techniques achieve a higher gender classification accuracy of 93 % and classify weight with 64% accuracy. The data reduction techniques are …
-
Object categorization using collections of parts and second order pooling features
… Vector Machines, L1 Logistic Regression and Boosted Decision Trees are presented and discussed. Methods to analyze confusion in these results are developed and results are presented. The Collections of Parts model is augmented with features from features generated by Second Order Pooling …
-
Predicting post-surgical opioid consumption using perioperative surgical data
… Using logistic regression and gradient boosted decision trees, model performance were evaluated at AUCs of 0.7270 and 0.7289 respectively.
-
Machine learning techniques for calorimeter cluster calibration of the CMS particle flow algorithm.
… excess. Machine learning techniques, such as Boosted Decision Trees (BDT) and Graph Neural Networks (GNN), are employed to calibrate PF energy clusters, improving both the response and the resolution of the measured energy. This thesis applies BDT to calibrate PF ECAL clusters, while GNN is …
-
Machine learning techniques for calorimeter cluster calibration of the CMS particle flow algorithm.
… excess. Machine learning techniques, such as Boosted Decision Trees (BDT) and Graph Neural Networks (GNN), are employed to calibrate PF energy clusters, improving both the response and the resolution of the measured energy. This thesis applies BDT to calibrate PF ECAL clusters, while GNN is …
-
FORECASTING INTERMITTENT DEMAND FOR AIRCRAFT SPARE PARTS USING MACHINE LEARNING
… traditional time-series baselines, and gradient boosted decision trees (XGBoost), and artificial neural networks as machine learning models. Accuracy is assessed using MASE as the primary metric, complemented by error distributions.Results demonstrate that machine learning models, particularly …
-
A search for the ttH (H → bb) channel at the Large Hadron Collider with the ATLAS detector using a matrix element method
… analyses utilizing Neural Networks and Boosted Decision Trees respectively. As no significant excess is found, an observed (expected) limit of 3.4 (2.2) times the Standard Model cross-section is determined at 95% confidence, using the CLs method, for the Neural Network analysis. For the …
-
Dynamic ensemble of classifiers and security relevant methods of android’s API : an empirical study
… this problem, being MLP, Random Forest, Gradient Boosted Decision Trees, and META-DES using Random Forest as pool generation gives the best results. We also find that, in general, Dynamic En- semble algorithms have a disadvantage compared to monolithic classifiers. Furthermore, this disadvantage …
-
Extending the Reach of Searches for Staus, Charginos and Neutralinos with the ATLAS Experiment at the Large Hadron Collider
… is presented. The search makes use of Boosted Decision Trees to classify the signal from the Standard Model backgrounds and in doing so significantly improves the sensitivity compared to a previous search using the same dataset. No significant differences between the observed data and …
-
Uncertainty-Based Methodology for the Development of Space Domain Awareness Architectures in Three-Body Regimes
… model. Of the models tested, the model based on boosted decision trees was found to have the best balance of speed and accuracy. This massive increase in computational efficiency enables designers to evaluate much larger volumes of design cases using the same hardware. The third identified …
-
Intelligent low-complexity widely deployable diagnostic tools for wireless edge device security using machine learning
… operating environment and make decentralized decisions, especially in remote deployments where it is difficult to have continued surveillance. To detect state-of-the-art subtle jamming attacks and enable decentralized decisions, this thesis exclusively utilizes correlated in-phase (I) and …