Evolutionary Selection of Kernels in Support Vector Machines

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@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 = "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