Active Pattern Recognition Using Genetic Programming

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

@PhdThesis{Teredesai:thesis,
  author =       "Ankur Mukund Teredesai",
  title =        "Active Pattern Recognition Using Genetic Programming",
  school =       "State University of New York at Buffalo",
  year =         "2002",
  address =      "Buffalo, New York, USA",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://phdtree.org/pdf/25644579-active-pattern-recognition-using-genetic-programming/",
  URL =          "http://search.proquest.com/docview/305243136",
  size =         "156 pages",
  abstract =     "The need for faster and robust methods for pattern
                 recognition and data mining is ever increasing.
                 Classical machine learning algorithms have always been
                 used in a variety of domains like optical character
                 recognition (OCR), speech recognition and information
                 extraction.

                 Different levels of informative detail can be present
                 in different regions of a pattern image. Classifiers
                 which selectively use features corresponding to
                 discriminating regions in making decisions for
                 particular classes are called active classifiers.
                 Design of active classifiers requires the pattern
                 recognition technique to blend feature discovery within
                 the classifier training phase. This dual task of
                 feature discovery and classifier training can be
                 combined to make the learning algorithm adaptive. This
                 dissertation titled Active Pattern Recognition using
                 Genetic Programming highlights the need for
                 applications to be adaptive. Traditional machine
                 learning algorithms for classification can be made
                 dynamic in terms of feature selection, computational
                 resource and scalability. This dissertation describes
                 how to make one such algorithm (Genetic Programming)
                 active, scalable and recurrent. The proposed extensions
                 are used to develop classifiers for handwritten digit
                 recognition. Genetic programming (GP) is a biologically
                 motivated machine learning technique like genetic
                 algorithms (GA). The essential idea is to represent
                 states (classification models in our case) as
                 chromosomes (encoded as expression trees) and to evolve
                 a population of new offspring trees by selectively
                 pairing parent trees. We first illustrate how GP based
                 active classifiers are developed for handwritten digit
                 recognition. A two-stage classification method
                 motivated by pair-wise confusion between digits is then
                 explored. Inspired by the performance for off-line hand
                 written digit classification, a strategy to classify
                 on-line handwritten digits based on off-line features
                 and GP is developed. We then present a recurrent-GP
                 framework which extends the proposed active pattern
                 recognition paradigm for applications where the length
                 of the feature vector is dynamic. One of the key
                 deterrents in using evolutionary computation techniques
                 for complex real-world applications in pattern
                 recognition and data mining is their non-scalable
                 nature in terms of computational requirements. We have
                 designed a new Efficient-GP technique to address these
                 issues. The dissertation concludes by discussing the
                 role of this paradigm in computational machine learning
                 theory.",
  notes =        "http://www.cedar.buffalo.edu/papers/dissertations.html
                 Doctoral Dissertations

                 supervisor: Venu Govindaraju
                 http://genealogy.math.ndsu.nodak.edu/id.php?id=104577
                 UMI Microform 3076535",
}

Genetic Programming entries for Ankur M Teredesai

Citations