Fast Error Estimation for Efficient Support Vector Machine Growing Articles uri icon

publication date

  • January 2010

start page

  • 1018

end page

  • 1023

issue

  • 4-6

volume

  • 73

international standard serial number (ISSN)

  • 0925-2312

electronic international standard serial number (EISSN)

  • 1872-8286

abstract

  • Support vector machines (SVMs) have become an off-the-shelf solution to solve many machine learning tasks but, unfortunately, the size of the resulting machines is quite often
    exceedingly large, which hampers their use in those practical
    applications demanding extremely fast response. Some methods exist to
    prune the models after training, but a full SVM model needs to be
    trained first, which usually represents a large computational cost.
    Furthermore, the reduction algorithms are prone to fall in local minima
    and also represent an additional non-negligible computational cost.
    Alternative procedures based on incrementally growing a semiparametric
    model provide a good compromise between complexity, machine size and
    performance. We investigate here the potential benefits of a fast error
    estimation (FEE) mechanism to improve the semiparametric SVM growing
    process. Precisely, we propose to use the FEE method to identify the
    best node to be added to the model in every growing step, by selecting
    the candidate with the lowest cross-validation error. We evaluate the
    proposed approach by evaluating the performance of the algorithm in
    benchmarks with real world datasets from the UCI machine learning
    repository.