Comparison of Multiclass SVM Decomposition Schemes for Visual Object Recognition
Lecture notes in computer sciencePublished 1 January 2005
Laine Kahsay, Friedhelm Schwenker, Günther Palm
Citations18
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
In the experiments, it is found that all the multiclass decomposing schemes for SVMs performed comparably very well with no significant statistical differences in cases of nonlinear kernels.
Abstract
We consider the problem of multiclass decomposition schemes for Support Vector Machines with Linear, Polynomial and RBF kernels. Our aim is to compare and discuss popular multiclass decomposing approaches such as the One versus the Rest, One versus One, Decision Directed Acyclic Graphs, Tree Structured, Error Correcting Output Codes. We conducted our experiments on benchmark datastes consisting of camera images of 3D objects. In our experiments we found that all the multiclass decomposing schemes for SVMs performed comparably very well with no significant statistical differences in cases of nonlinear kernels. In case of linear kernels the multiclass schemes OvR, OvO and DDAG outperform Tree Structured and ECOC.
Keywords
Computer Science
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