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

  • University of the Basque Country

Producció científica: Capítol del llibre/Acta del congrésActa de congrésAvaluat per experts

6 Cites (Scopus)

Resum

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 originalAnglès
Títol de la publicacióAdvances in Artificial Intelligence - 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011, Proceedings
EditorSpringer Verlag
Pàgines134-144
Nombre de pàgines11
ISBN (imprès)9783642252730
DOIs
Estat de la publicacióData de publicació - 2011
Publicat externament
Esdeveniment14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011 - La Laguna, Espanya
Durada: 7 de nov. 201111 de nov. 2011

Sèrie de publicacions

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum7023 LNAI
ISSN (imprès)0302-9743
ISSN (electrònic)1611-3349

Congrés

Congrés14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011
País/TerritoriEspanya
CiutatLa Laguna
Període7/11/1111/11/11

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