Evolutionary Selection of Kernels in Support Vector Machines

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Thadani:2006:ADCOM,
  title =        "Evolutionary Selection of Kernels in Support Vector
                 Machines",
  author =       "Kanchan Thadani and Ashutosh and V. K. Jayaraman and 
                 V. Sundararajan",
  booktitle =    "International Conference on Advanced Computing and
                 Communications, ADCOM 2006",
  year =         "2006",
  month =        dec,
  pages =        "19--24",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, bank transaction data set,
                 cancer data, evolutionary algorithm, kernel function,
                 machine learning algorithm, pattern classification,
                 support vector machine, bank data processing, cancer,
                 evolutionary computation, genetics, learning
                 (artificial intelligence), medical computing, pattern
                 classification, support vector machines",
  URL =          "http://ieeexplore.ieee.org/iel5/4289832/4289833/04289849.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.5378",
  URL =          "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4289849&userType=&tag=1",
  DOI =          "doi:10.1109/ADCOM.2006.4289849",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:10.1.1.138.5378",
  abstract =     "A machine learning algorithm using evolutionary
                 algorithms and Support Vector Machines is presented.
                 The kernel function of support vector machines are
                 evolved using recently introduced Gene Expression
                 Programming algorithms. This technique trains a support
                 vector machine with the kernel function most suitable
                 for the training data set rather than pre-specifying
                 the kernel function. The fitness of the kernel is
                 measured by calculating cross validation accuracy. SVM
                 trained with the fittest kernels is then used to
                 classify previously unseen data. The algorithm is
                 elucidated using preliminary case studies for
                 classification of cancer data and bank transaction data
                 set. It is shown that the Evolutionary Support Vector
                 Machine has good generalization properties when
                 compared with Support Vector Machines using standard
                 (polynomial and radial basis) kernel functions.",
  notes =        "Kanchan Thadani is with the Scientific and Engineering
                 Computing Group, Centre For Development of Advanced
                 Computing, Pune University campus, Pune-411007, India
                 Ashutosh is with the Persistent System Pvt.Ltd.,
                 Persistent Towers, Erandwane, Pune, India

                 V.K. Jayaraman is with the Chemical Engineering
                 Division, National Chemical Laboratory, Pashan,
                 Pune-411008, India

                 V. Sundararajan is with the Scientific and Engineering
                 Computing Group, Centre For Development of Advanced
                 Computing, Pune University campus, Pune-411007, India",
}

Genetic Programming entries for Kanchan Thadani Ashutosh Valadi Krishnamoorthy Jayaraman Vijayraghavan Sundararajan

Citations