id: 05569141
dt: a
an: 05569141
au: Barbero, Álvaro; Dorronsoro, José R.
ti: A simple maximum gain algorithm for support vector regression.
so: Cabestany, Joan (ed.) et al., Bio-inspired systems: Computational and
    ambient intelligence. 10th international work-conference on artificial
    neural networks, IWANN 2009, Salamanca, Spain, June 10‒12, 2009.
    Proceedings. Part I. Berlin: Springer (ISBN 978-3-642-02477-1/pbk).
    Lecture Notes in Computer Science 5517, 73-80 (2009).
py: 2009
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-02478-8_10
ab: Summary: Shevade’s et al. Modification 2 is one of the most widely used
    algorithms to build Support Vector Regression (SVR) models. It selects
    as a size 2 working set the index pair giving the maximum KKT violation
    and combines it with the updating heuristics of Smola and Schölkopf
    enforcing at each training iteration a $α_i α^*_i =0$ condition. In
    this work we shall present an alternative, much simpler procedure that
    selects the updating indices as those giving a maximum gain in the SVR
    dual function. While we do not try to enforce the $α_i α^*_i =0$
    condition, we show that it will hold at each iteration provided it does
    so at the starting multipliers. We will numerically show that the
    proposed procedure requires essentially the same number of iterations
    than Modification 2 having thus the same time performance while being
    much simpler to code.
rv: