Top (journal of operations research)

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  Año 2024, Vol. 32, Número Extraordinario 3. Dedicado a: Mathematical Optimization and Machine Learning

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    Guest editorial to the Special Issue on Machine Learning and Mathematical Optimization in TOP-Transactions in Operations Research

    Emilio Carrizosa Priego , Dolores Romero Morales

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    págs. 351-353

    
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    Predicting the demographics of Twitter users with programmatic weak supervision

    Jonathan Tonglet, Astrid Jehoul, Manon Reusens, Michael Reusens, Bart Baesens

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    págs. 354-390

    
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    Mixed-integer quadratic optimization and iterative clustering techniques for semi-supervised support vector machines

    Jan Pablo Burgard, Maria Eduarda Pinheiro, Martin Schmidt

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    págs. 391-428

    
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    Disagreement amongst counterfactual explanations: how transparency can be misleading

    Dieter Brughmans, Lissa Melis, David Martens

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    págs. 429-462

    
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    Adapting support vector optimisation algorithms to textual gender classifcation

    Javier Gomez, Cesar Alfaro, Felipe Ortega, Javier Martínez Moguerza , Maria Jesus Algar, Raul Moreno

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    págs. 463-488

    
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    Learning‑assisted optimization for transmission switching

    Salvador Pineda Morente , Juan Miguel Morales Gonzalez , María Asunción Jiménez Cordero

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    págs. 489-516

    
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    Bolstering stochastic gradient descent with model building

    Ş. İlker Birbil, Ozgür Martin, Gönenç Onay, Figen Öztoprak

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    págs. 517-536

    
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    Gaining insight into crew rostering instances through ML-based sequential assignment

    Philippe Racette, Frédéric Quesnel, Andrea Lodi, François Soumis

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    págs. 537-578

    
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    Tuning parameters of deep neural network training algorithms pays off: a computational study

    Corrado Coppola, Lorenzo Papa, Marco Boresta, Irene Amerini, Laura Palagi

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    págs. 579-620