Guided Genetic Programming Models for Multiple Instance Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Zafra:thesis,
  author =       "Amelia Zafra",
  title =        "Guided Genetic Programming Models for Multiple
                 Instance Learning",
  school =       "University of Cordoba",
  year =         "2009",
  address =      "Spain",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.uco.es/grupos/kdis/index.php?option=com_jresearch&view=thesis&task=show&id=2&Itemid=51&lang=en",
  abstract =     "This work focuses on the design of grammatical genetic
                 programming models for solving different paradigm of
                 learning applications with multiple instances.

                 First, we review the status of art of this learning.
                 Following this review, we find that almost all learning
                 paradigms used in machine learning have been extended
                 to this paradigm, but there are no proposals of
                 Evolutionary Algorithms (EAs) in this learning
                 framework. EAs are a good alternative in different
                 learning paradigms which have been applied, the large
                 number of publications appeared since its appearance is
                 an evidence of this popularity. In this work
                 grammatical genetic programming methods both mono-and
                 multi-objective are introduced for the resolution of
                 different applications. In first place, an experimental
                 study using benchmark data sets is carried out to
                 demonstrate their effectiveness with respect to the
                 most relevant proposals done over the years. Then, the
                 models are applied over two real problems: web index
                 page recommendation and prediction of a student's
                 academic performance considering the work developed in
                 the educational platform; these problems approached
                 from a traditional supervised learning contain many
                 missing values making difficult the correct
                 classification. Using MIL, we seek a more flexible
                 representation to solve them.",
  notes =        "in Spanish

                 Supervisor: Sebastian Ventura",
}

Genetic Programming entries for Amelia Zafra Gomez

Citations