Concha Bielza

TL;DR: Modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization, are presented.

TL;DR: A possible taxonomical solution for classifying GABAergic interneurons of the cerebral cortex based on a novel, web-based interactive system that allows experts to classify neurons with pre-determined criteria is described.

TL;DR: This study provides a survey on state‐of‐the‐art multi‐output regression methods, that are categorized as problem transformation and algorithm adaptation methods, and presents the mostly used performance evaluation measures, publicly available data sets for multi-output regression real‐world problems, as well as open‐source software frameworks.

TL;DR: This paper presents flexible algorithms for learning MBC structures from data based on filter, wrapper and hybrid approaches, and derives theoretical results on how to minimize the expected loss under standard 0-1 loss functions.

TL;DR: This article surveys the whole set of discrete Bayesian network classifiers devised to date, organized in increasing order of structure complexity: naive Bayes, selective naive Baye, seminaive Bayer, one-dependence Bayesian classifiers, k-dependency Bayesianclassifiers, Bayes network-augmented naiveBayes, Markov blanket-based Bayesian Classifier, unrestricted BayesianClassifiers, and Bayesian multinets.