Optimization approaches to Supervised Classification

By A. Pedro Duarte Silva. 2017

29 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Mathematical optimization in classification and regression trees (2021, background): “(2020), Liberti (2020) for surveys reviewing the use of Mathematical Optimization in Machine Learning, and Carrizosa and Romero Morales (2013), Duarte Silva (2017), Palagi (2019), and Piccialli and Sciandrone (2018) for surveys focusing on specific methodologies.”
Learning Optimal Classification Trees Using a Binary Linear Program Formulation (2019, methodology): “…various Mathematical Optimization methods to build better quality decision trees, e.g., (Bennett and Blue 1996; Bessiere, Hebrard, and OSullivan 2009; Verwer and Zhang 2017; Bertsimas and Dunn 2017; Silva 2017; Dash, G ¨ unl ¨ uk, and Wei 2018; Blanquero et al. 2018a; 2018b; Firat et al. 2018).”
Global optimization issues in deep network regression: an overview (2018, methodology): “We refer to [41] for a survey on semi-supervised methods which are not covered in this paper, to [36,53] and references therein for complete surveys on mathematical optimization for SVM, and to [107] for a survey on solution approaches for the convex training problem of different SVMs.”
Sparsity in Optimal Randomized Classification Trees (2020, background): “…affect the overall learning performance. he major advances in Mathematical Optimization ( Carrizosa & omero Morales, 2013; Olafsson, Li, & Wu, 2008; Silva, 2017 ) have ed to different approaches to build decision trees with some verall optimality criterion, called hereafter optimal classification…”