id: 06092013
dt: j
an: 06092013
au: Rodriguez-Lujan, Irene; Cruz, Carlos Santa; Huerta, Ramon
ti: Hierarchical linear support vector machine.
so: Pattern Recognition 45, No. 12, 4414-4427 (2012).
py: 2012
pu: Elsevier Science Ltd. (Pergamon), Oxford
la: EN
cc: 
ut: large-scale learning; real-time prediction; support vector machine;
    decision tree; Pegasos algorithm
ci: 
li: doi:10.1016/j.patcog.2012.06.002
ab: Summary: The increasing size and dimensionality of real-world datasets make
    it necessary to design efficient algorithms not only in the training
    process but also in the prediction phase. In applications such as
    credit card fraud detection, the classifier needs to predict an event
    in 10 ms at most. In these environments the speed of the prediction
    constraints heavily outweighs the training costs. We propose a new
    classification method, called a hierarchical linear support vector
    machine (H-LSVM), based on the construction of an oblique decision tree
    in which the node split is obtained as a linear support vector machine.
    Although other methods have been proposed to break the data space down
    in subregions to speed up Support Vector Machines, the H-LSVM algorithm
    represents a very simple and efficient model in training but mainly in
    prediction for large-scale datasets. Only a few hyperplanes need to be
    evaluated in the prediction step, no kernel computation is required and
    the tree structure makes parallelization possible. In experiments with
    medium and large datasets, the H-LSVM reduces the prediction cost
    considerably while achieving classification results closer to the
    non-linear SVM than that of the linear case.
rv: