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Learning naive Bayes models for multiple-instance learning with label proportions

  • University of the Basque Country

Producción científica: Capítulo del libro/Acta de congresoActa de congresorevisión exhaustiva

6 Citas (Scopus)

Resumen

This paper deals with the problem of multi-instance learning when label proportions are provided. In this classification problem, the instances of the dataset are divided into disjoint groups, where there is no certainty about the labels associated with individual samples. However, in each group the number of instances that belong to each class is known. We propose several versions of an EM-algorithm that learns naive Bayes models to deal with the exposed problem. The proposed algorithms are evaluated on synthetic and real datasets, and compared with state-of-the-art approaches. The obtained results show a competitive behaviour of our proposals.

Idioma originalInglés
Título de la publicación alojadaAdvances in Artificial Intelligence - 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011, Proceedings
EditorialSpringer Verlag
Páginas134-144
Número de páginas11
ISBN (versión impresa)9783642252730
DOI
EstadoPublicada - 2011
Publicado de forma externa
Evento14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011 - La Laguna, Espana
Duración: 7 nov 201111 nov 2011

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen7023 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011
País/TerritorioEspana
CiudadLa Laguna
Período7/11/1111/11/11

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