Centers and Invariant Points of Convex Bodies. - Mark J. Kaiser; Thomas L. Morin; Theodore B. Trafalis (Applied Geometry And Discrete Mathematics, 1990)

Neural network training via an affine scaling quadratic optimization algorithm. - Theodore B. Trafalis; Nicolas P. Couellan (Neural Networks, 1996)

Training of supervised neural networks via a nonlinear primal-dual interior-point method. - Theodore B. Trafalis; Nicolas P. Couellan; Sébastien C. Bertrand (ICNN, 1997)

Neural Networks Approaches for Combinatorial Optimization Problems. - Theodore B. Trafalis; Suat Kasap (Handbook of Combinatorial Optimization, 1999)

Support Vector Machine for Regression and Applications to Financial Forecasting. - Theodore B. Trafalis; Huseyin Ince (IJCNN (6), 2000)

A novel metaheuristics approach for continuous global optimization. - Theodore B. Trafalis; Suat Kasap (J. Glob. Optim., 2002)

An Analytic Center Machine. - Theodore B. Trafalis; Alexander M. Malyscheff (Mach. Learn., 2002)

Tornado Detection with Support Vector Machines. - Theodore B. Trafalis; Huseyin Ince; Michael B. Richman (International Conference on Computational Science, 2003)

Support vector machines and the electoral college. - Alexander M. Malyscheff; Theodore B. Trafalis (IJCNN, 2003)

Robust optimization in support vector machine training with bounded errors. - Theodore B. Trafalis; Samir A. Alwazzi (IJCNN, 2003)

Rule-Based Support Vector Machine Classifiers Applied to Tornado Prediction. - Theodore B. Trafalis; Budi Santosa; Michael B. Richman (International Conference on Computational Science, 2004)

Kernel principal component analysis and support vector machines for stock price prediction. - Huseyin Ince; Theodore B. Trafalis (IJCNN, 2004)

Determination of the optimal batch size in incremental approaches: an application to tornado detection. - Hyung-Jin Son; Theodore B. Trafalis; Michael B. Richman (IJCNN, 2005)

Maximum margin classifiers with noisy data: a robust optimization approach. - Theodore B. Trafalis; Robin C. Gilbert (IJCNN, 2005)

Learning networks in rainfall estimation. - Theodore B. Trafalis; Budi Santosa; Michael B. Richman (Comput. Manag. Sci., 2005)

Knowledge-Based Multiclass Support Vector Machines Applied to Vertical Two-Phase Flow. - Olutayo O. Oladunni; Theodore B. Trafalis; Dimitrios V. Papavassiliou (International Conference on Computational Science (1), 2006)

Detection of Tornados Using an Incremental Revised Support Vector Machine with Filters. - Hyung-Jin Son; Theodore B. Trafalis (International Conference on Computational Science (3), 2006)

A Pairwise Reduced Kernel-based Multi-classification Tikhonov Regularization Machine. - Olutayo O. Oladunni; Theodore B. Trafalis (IJCNN, 2006)

Foreword. - Theodore B. Trafalis (Comput. Manag. Sci., 2006)

A hybrid model for exchange rate prediction. - Huseyin Ince; Theodore B. Trafalis (Decis. Support Syst., 2006)

Robust classification and regression using support vector machines. - Theodore B. Trafalis; Robin C. Gilbert (Eur. J. Oper. Res., 2006)

Kernel methods for short-term portfolio management. - Huseyin Ince; Theodore B. Trafalis (Expert Syst. Appl., 2006)

Learning networks for tornado detection. - Theodore B. Trafalis; Budi Santosa; Michael B. Richman (Int. J. Gen. Syst., 2006)

Regularized Knowledge-Based Kernel Machine. - Olutayo O. Oladunni; Theodore B. Trafalis (International Conference on Computational Science (1), 2007)

Active Learning with Support Vector Machines for Tornado Prediction. - Theodore B. Trafalis; Indra Adrianto; Michael B. Richman (International Conference on Computational Science (1), 2007)

Regularization Based Classification Models. - Olutayo O. Oladunni; Theodore B. Trafalis (IJCNN, 2007)

Robust multiclass kernel-based classifiers. - Budi Santosa; Theodore B. Trafalis (Comput. Optim. Appl., 2007)

Support vector regression with noisy data: a second order cone programming approach. - Theodore B. Trafalis; Samir A. Alwazzi (Int. J. Gen. Syst., 2007)

Robust support vector machines for classification and computational issues. - Theodore B. Trafalis; Robin C. Gilbert (Optim. Methods Softw., 2007)

Real-time prediction of order flowtimes using support vector regression. - Abdulrahman Alenezi; Scott A. Moses; Theodore B. Trafalis (Comput. Oper. Res., 2008)

Short term forecasting with support vector machines and application to stock price prediction. - Huseyin Ince; Theodore B. Trafalis (Int. J. Gen. Syst., 2008)

Guest Editorial. - O. Erhun Kundakcioglu; Marcello Sanguineti; Theodore B. Trafalis (Comput. Manag. Sci., 2009)

A nonlinear multi-classification knowledge-based kernel machine. - Olutayo O. Oladunni; Theodore B. Trafalis (Comput. Manag. Sci., 2009)

A regularized pairwise multi-classification knowledge-based machine and applications. - Olutayo O. Oladunni; Theodore B. Trafalis (Eur. J. Oper. Res., 2009)

Support vector machines for spatiotemporal tornado prediction. - Indra Adrianto; Theodore B. Trafalis; Valliappa Lakshmanan (Int. J. Gen. Syst., 2009)

Quadratic programming formulations for classificationand regression. - Robin C. Gilbert; Theodore B. Trafalis (Optim. Methods Softw., 2009)

Neural Networks for Combinatorial Optimization. - Theodore B. Trafalis; Suat Kasap (Encyclopedia of Optimization, 2009)

Support vector machine classification with noisy data: a second order cone programming approach. - Theodore B. Trafalis; Samir A. Alwazzi (Int. J. Gen. Syst., 2010)

The p-Centre machine for regression analysis. - Indra Adrianto; Theodore B. Trafalis (Optim. Methods Softw., 2010)

Kernel logistic regression using truncated Newton method. - Maher Maalouf; Theodore B. Trafalis; Indra Adrianto (Comput. Manag. Sci., 2011)

Linear classification tikhonov regularization knowledge-based support vector machine for tornado forecasting. - Theodore B. Trafalis; Olutayo O. Oladunni; Michael B. Richman (Comput. Manag. Sci., 2011)

Robust weighted kernel logistic regression in imbalanced and rare events data. - Maher Maalouf; Theodore B. Trafalis (Comput. Stat. Data Anal., 2011)

Rare events and imbalanced datasets: an overview. - Maher Maalouf; Theodore B. Trafalis (Int. J. Data Min. Model. Manag., 2011)

Single-phase fluid flow classification via learning models. - Olutayo O. Oladunni; Theodore B. Trafalis (Int. J. Gen. Syst., 2011)

Time-series Analysis for Detecting Structure Changes and Suspicious Accounting Activities in Public Software Companies. - Zhen Zhang; Theodore B. Trafalis (Complex Adaptive Systems, 2013)

An incremental primal-dual method for nonlinear programming with special structure. - Nicolas P. Couellan; Theodore B. Trafalis (Optim. Lett., 2013)

On-line SVM learning via an incremental primal-dual technique. - Nicolas P. Couellan; Theodore B. Trafalis (Optim. Methods Softw., 2013)

Preface: Special volume on data mining and informatics. - Victoria C. P. Chen; Seoung Bum Kim; Theodore B. Trafalis (Ann. Oper. Res., 2014)

Machine-learning classifiers for imbalanced tornado data. - Theodore B. Trafalis; Indra Adrianto; Michael B. Richman; S. Lakshmivarahan (Comput. Manag. Sci., 2014)

A Bayesian beta kernel model for binary classification and online learning problems. - Cameron A. MacKenzie; Theodore B. Trafalis; Kash Barker (Stat. Anal. Data Min., 2014)

A Single-Facility Manifold Location Routing Problem with an Application to Supply Chain Management and Robotics. - Emre Tokgöz; Iddrisu Awudu; Theodore B. Trafalis (MOD, 2015)

A heuristic algorithm to solve the single-facility location routing problem on Riemannian surfaces. - Emre Tokgöz; Samir A. Alwazzi; Theodore B. Trafalis (Comput. Manag. Sci., 2015)

Thyroid Nodule Benignty Prediction by Deep Feature Extraction. - XueYan Mei; Xiaomeng Dong; Timothy Deyer; Jingyi Zeng; Theodore B. Trafalis; Yan Fang (BIBE, 2017)

2-Facility manifold location routing problem. - Emre Tokgöz; Theodore B. Trafalis (Optim. Lett., 2017)

A data-driven kernel method assimilation technique for geophysical modelling. - Robin C. Gilbert; Theodore B. Trafalis; Michael B. Richman; Lance M. Leslie (Optim. Methods Softw., 2017)

Logistic regression in large rare events and imbalanced data: A performance comparison of prior correction and weighting methods. - Maher Maalouf; Dirar Homouz; Theodore B. Trafalis (Comput. Intell., 2018)

Kernel classification using a linear programming approach. - Alexander M. Malyscheff; Theodore B. Trafalis (Ann. Math. Artif. Intell., 2020)

Affinely adjustable robust optimization under dynamic uncertainty set for a novel robust closed-loop supply chain. - Ismail I. Almaraj; Theodore B. Trafalis (Comput. Ind. Eng., 2020)

An approximation to max min fairness in multi commodity networks. - Hamoud S. Bin Obaid; Theodore B. Trafalis (Comput. Manag. Sci., 2020)

Constructing an Efficient Machine Learning Model for Tornado Prediction. - Fuad Aleskerov; Sergey Demin; Michael B. Richman; Sergey Shvydun; Theodore B. Trafalis; Vyacheslav Yakuba (Int. J. Inf. Technol. Decis. Mak., 2020)

Imbalanced Learning with Parametric Linear Programming Support Vector Machine for Weather Data Application. - Elaheh Jafarigol; Theodore B. Trafalis (SN Comput. Sci., 2020)

Deep MLP-CNN Model Using Mixed-Data to Distinguish between COVID-19 and Non-COVID-19 Patients. - Md. Manjurul Ahsan; Tasfiq E. Alam; Theodore B. Trafalis; Pedro Huebner (Symmetry, 2020)

Learning Reliable Rules under Class Imbalance. - Dimitrios I. Diochnos; Theodore B. Trafalis (SDM, 2021)

Optimizing Data Augmentation Policy Through Random Unidimensional Search. - Xiaomeng Dong; Michael Potter; Gaurav Kumar; Yun-Chan Tsai; V. Ratna Saripalli; Theodore B. Trafalis (LION, 2022)

Autonomous Learning Rate Optimization for Deep Learning. - Xiaomeng Dong; Tao Tan; Michael Potter; Yun-Chan Tsai; Gaurav Kumar; V. Ratna Saripalli; Theodore B. Trafalis (LION, 2022)

A robust optimization approach in a multi-objective closed-loop supply chain model under imperfect quality production. - Ismail I. Almaraj; Theodore B. Trafalis (Ann. Oper. Res., 2022)

A robust global optimisation framework for stochastic integrated refinery planning with demand and price uncertainties. - Mahmud R. Siamizade; Theodore B. Trafalis (Int. J. Math. Oper. Res., 2022)

Learning-Based Nonlinear H∞ Control via Game-Theoretic Differential Dynamic Programming. - Wei Sun; Theodore B. Trafalis (ACC, 2023)

A Review of Machine Learning Techniques in Imbalanced Data and Future Trends. - Elaheh Jafarigol; Theodore B. Trafalis (CoRR, 2023)

Religious Affiliation in the Twenty-First Century: A Machine Learning Perspective on the World Value Survey. - Elaheh Jafarigol; William Keely; Tess Hartog; Tom Welborn; Peyman Hekmatpour; Theodore B. Trafalis (CoRR, 2023)

The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning. - Elaheh Jafarigol; Theodore B. Trafalis (CoRR, 2023)

Exploring Machine Learning Models for Federated Learning: A Review of Approaches, Performance, and Limitations. - Elaheh Jafarigol; Theodore B. Trafalis; Talayeh Razzaghi; Mona Zamankhani (CoRR, 2023)

Fairness in Machine Learning: A Survey Part 1. - Karen Roberts-Licklider; Theodore B. Trafalis (DIS, 2024)

Fairness in Optimization and ML: A Survey Part 2. - Karen Roberts-Licklider; Theodore B. Trafalis (DIS, 2024)

Machine Learning Techniques with Fairness for Prediction of Completion of Drug and Alcohol Rehabilitation. - Karen Roberts-Licklider; Theodore B. Trafalis (CoRR, 2024)

A distributed approach to meteorological predictions: addressing data imbalance in precipitation prediction models through federated learning and GANs. - Elaheh Jafarigol; Theodore B. Trafalis (Comput. Manag. Sci., 2024)

To raise or not to raise: the autonomous learning rate question. - Xiaomeng Dong; Tao Tan; Michael Potter; Yun-Chan Tsai; Gaurav Kumar; V. Ratna Saripalli; Theodore B. Trafalis (Ann. Math. Artif. Intell., 2024)

Using Federated Machine Learning in Predictive Maintenance of Jet Engines. - Asaph Matheus Barbosa; Thao Vy Nhat Ngo; Elaheh Jafarigol; Theodore B. Trafalis; Emuobosa P. Ojoboh (CoRR, 2025)

H∞-Optimal Control via Game-Theoretic Differential Dynamic Programming and Gaussian Processes. - Wei Sun 032; Theodore B. Trafalis (J. Optim. Theory Appl., 2025)

Optimizing Treatment Facility Locations in Oklahoma Using Haversine, Euclidean, Manhattan, and Chebyshev Distance Optimization. - Karen Roberts-Licklider; Theodore B. Trafalis (Oper. Res. Forum, 2025)