Observatorio de I+D+i UPM

Memorias de investigación
Capítulo de libro:
Learning conditional linear Gaussian classifiers with probabilistic class labels
Año:2013
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
We study the problem of learning Bayesian classifiers (BC) when the true class label of the training instances is not known, and is substituted by a probability distribution over the class labels for each instance. This scenario can arise, e.g., when a group of experts is asked to individually provide a class label for each instance. We particularize the generalized expectation maximization (GEM) algorithm in [1] to learn BCs with different structural complexities: naive Bayes, averaged one-dependence estimators or general conditional linear Gaussian classifiers. An evaluation conducted on eight datasets shows that BCs learned with GEM perform better than those using either the classical Expectation Maximization algorithm or potentially wrong class labels. BCs achieve similar results to the multivariate Gaussian classifier without having to estimate the full covariance matrices.
Internacional
Si
DOI
Edición del Libro
Editorial del Libro
Springer
ISBN
978-3-642-40642-3
Serie
0302-9743
Título del Libro
Advances in Artificial Intelligence, Lecture Notes in Artificial Intelligence 8109
Desde página
139
Hasta página
148
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Pedro Luis Lopez Cruz (UPM)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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