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.
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Showing 1 to 20 of 51 for “"Ensemble methods"”.
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Ensemble Methods for Anomaly Detection
… </p> <p>We address this problem, proposing ensemble anomaly detection techniques that perform well in many applications, with four major contributions: using bootstrapping to better detect anomalies on multiple subsamples, sequential application of diverse detection</p> <p>algorithms, a …
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Randomness In Tree Ensemble Methods
Tree ensembles have proven to be a popular and powerful tool for predictive modeling tasks. The theory behind several of these methods (e.g. boosting) has received considerable attention. However, other tree ensemble techniques (e.g. bagging, random forests) have attracted limited theoretical …
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Randomness In Tree Ensemble Methods
Tree ensembles have proven to be a popular and powerful tool for predictive modeling tasks. The theory behind several of these methods (e.g. boosting) has received considerable attention. However, other tree ensemble techniques (e.g. bagging, random forests) have attracted limited theoretical …
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An Efficient Ranking and Classification Method for Linear Functions, Kernel Functions, Decision Trees, and Ensemble Methods
Structural algorithms incorporate the interdependence of outputs into the prediction, the loss, or both. Frank-Wolfe optimizations of pairwise losses and Gaussian conditional random fields for multivariate output regression are two such structural algorithms. Pairwise losses are standard 0-1 …
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Ensemble methods in computational protein and ligand design : applications to the Fc[gamma] immunoglobulin, HIV-1 protease, and ketol-acid reductoisomerase systems
This thesis explores the use of ensemble, free energy models in the study and design of molecular, biochemical systems. We use physics based computational models to analyze the molecular basis of binding affinity in the context of protein-protein and protein-ligand binding as well as reaction rate …
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Ensembles of Adaptive One-Factor at-a-Time experiments : methods, evaluation, and theory
… statistical prediction practices referred to as Ensemble Methods, to extend Adaptive One-Factor-at-a-Time (aOFAT) experimentation. The algorithm is developed for an input space where each variable assumes two or more discrete levels. Ensemble methods are common data mining procedures in which a …
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Nonparametric High-dimensional Models: Sparsity, Efficiency, Interpretability
This thesis explores ensemble methods in machine learning, a technique that builds a predictive model by jointly training simpler base models. It examines three types of ensemble methods: additive models, tree ensembles, and mixtures of experts. Each ensemble method is characterized by a specific …
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Efficient High Order Ensemble for Fluid Flow
<p>"This thesis proposes efficient ensemble-based algorithms for solving the full and reduced Magnetohydrodynamics (MHD) equations. The proposed ensemble methods require solving only one linear system with multiple right-hand sides for different realizations, reducing computational cost and …
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Scalable Representation Learning: On Data-scarcity, Uncertainty and Symmetry
… these issues by introducing novel tools and methods that augment traditional deep learning. We explore various strategies for solving the main bottlenecks of traditional deep learning, which includes incorporating prior known symmetries and inductive biases of the problem, utilizing Bayesian …
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Machine Learning and Field Inversion approaches to Data-Driven Turbulence Modeling
… is obtained by means of both variational and ensemble methods. The second approach is to infer the Reynolds stress field for a flow of interest from limited velocity or pressure measurements of the same flow. Here, this field inversion is done using a Monte Carlo Bayesian procedure and the …
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Machine learning ensemble method for discovering knowledge from big data
… machine learning and data mining to develop new methods and techniques for analysing big data effectively and efficiently. Ensemble methods represent an attractive approach in dealing with the problem of mining large datasets because of their accuracy and ability of utilizing the …
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A new filtering method for improving the quality of variant discovery
… (VQSR) or Hard Filtering (HF). However, these methods are very user-dependent and fail to run in some cases. We propose Variant Ensemble Filter (VEF), a variant filtering tool based on decision tree ensemble methods that overcomes the main drawbacks of VQSR and HF. Contrary to these methods, we …
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Cyber security risk analysis framework : network traffic anomaly detection
… ARIMA, TBATS, Double-Seasonal Holt-Winters, and Ensemble methods) and Long Short-Term Memory Recurrent Neural Network algorithm. Upon creating the baselines and forecasting network traffic trends, the anomaly detection algorithm was implemented using specific thresholds to detect network traffic …
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Toward accurate free energy calculations in biomolecular simulation: advances in Markov model reweighting and expanded ensemble method
… into their folded and unfolded conformational ensembles, which are often challenging to obtain from simulations alone due to timescale limitations and force field inaccuracies. This dissertation advances computational tools for molecular design by integrating molecular dynamics (MD) simulations …
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Decoupling methods for the time-dependent Navier-Stokes-Darcy interface model
<p>"In this research, several decoupling methods are developed and analyzed for approximating the solution of time-dependent Navier-Stokes-Darcy (NS-Darcy) interface problems. This research on decoupling methods is motivated to efficiently solve the complex Stokes-Darcy or NS-Darcy type models, …
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Intelligible models for learning categorical data via generalized fourier spectrum
… sophisticated models such as neural networks and ensemble methods have achieved impressive predictive performances. However, these models are hard to interpret and usually used as a blackbox. In applications where an explanation is required in addition to a prediction, linear models (e.g. Linear …
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Multi-criteria optimisation for complex learning prediction systems.
… its performance is compared to three well-known ensemble methods. Next, the effect of weighing the components of the MCMLPS and combining them is examined using six fusion methods. The results showed that, including the similarity metric used to divide the data into local regions in weighing the …
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A comparative evaluation of machine learning models for stock price prediction and uncertainity estimation
… Perceptron (MLP), and a hybrid stacking ensemble composed of multiple base learners. For uncertainty quantification, three interval prediction methods were used: Bootstrap Residuals, Quantile Regression Forests (QRF), and Conformalised Quantile Regression (CQR). The analysis used …
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A Multitask Deep Learning Framework for Clinical Decision-Making in Assisted Reproductive Technology
… pipeline evaluates classical statistical models, ensemble methods (XGBoost), and novel architectures, including TabPFN, an attention-based probabilistic model that achieved comparable performance to top-performing baselines. To enhance clinical trust, we apply SHapley Additive exPlanations (SHAP) …
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