Finding Optimal Combination of Kernels using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Jyothi Korra",
  title =        "Finding Optimal Combination of Kernels using Genetic
  howpublished = "ArXiv",
  year =         "2016",
  keywords =     "genetic algorithms, genetic programming, SVM?",
  bibdate =      "2016-05-02",
  bibsource =    "DBLP,
  URL =          "",
  abstract =     "In Computer Vision, problem of identifying or
                 classifying the objects present in an image is called
                 Object Categorization. It is a challenging problem,
                 especially when the images have clutter background,
                 occlusions or different lighting conditions. Many
                 vision features have been proposed which aid object
                 categorization even in such adverse conditions. Past
                 research has shown that, employing multiple features
                 rather than any single features leads to better
                 recognition. Multiple Kernel Learning (MKL) framework
                 has been developed for learning an optimal combination
                 of features for object categorization. Existing MKL
                 methods use linear combination of base kernels which
                 may not be optimal for object categorization.
                 Real-world object categorization may need to consider
                 complex combination of kernels(non-linear) and not only
                 linear combination. Evolving non-linear functions of
                 base kernels using Genetic Programming is proposed in
                 this report. Experiment results show that non-kernel
                 generated using genetic programming gives good accuracy
                 as compared to linear combination of kernels",

Genetic Programming entries for Jyothi Korra