BSVM2 | ![]() |
Multi-class BSVM with L2-soft margin.
Synopsis:
model = bsvm2( data, options )
Description:
This function trains the multi-class SVM classifier based
on BSVM formulation (bias added to the objective function) and
L2-soft margin penalization of misclassifications [Franc02][Hsu02].
The quadratic programming criterion can be optimzed by one of the
following algorithms:
mdm ... Mitchell-Demyanov-Malozemov
kozinec ... Kozinec's algorithm.
npa ... Nearest Point Algorithm.
Input:
data [struct] Training data:
.X [dim x num_data] Training vectors.
.y [1 x num_data] Labels (1,2,...,nclass).
options [struct] Control parameters:
.ker [string] Kernel identifier. See 'help kernel'.
.arg [1 x nargs] Kernel argument(s).
.C [1x1] Regularization constant.
.solver [string] Used QP solver: 'kozinec', 'mdm', 'npa' (default).
.tmax [1x1] Maximal number of iterations.
.tolabs [1x1] Absolute tolerance stopping condition (default 0.0).
.tolrel [1x1] Relative tolerance stopping condition (default 0.001).
Output:
model [struct] Multi-class SVM classifier:
.Alpha [nsv x nclass] Weights.
.b [nclass x 1] Biases.
.sv.X [dim x nsv] Support vectors.
.nsv [1x1] Number of support vectors.
.options [struct] Copy of input options.
.t [1x1] Number of iterations.
.UB [1x1] Upper bound on the optimal solution.
.LB [1x1] Lower bound on the optimal solution.
.History [2 x (t+1)] UB and LB with respect to t.
Example:
data = load('pentagon');
options = struct('ker','rbf','arg',1,'C',10,'solver','npa');
model = bsvm2( data, options )
figure;
ppatterns(data); ppatterns( model.sv.X, 'ok',12);
pboundary( model );
See also
SVMCLASS, OAASVM, OAOSVM.
About: Statistical Pattern Recognition Toolbox
(C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac
Czech Technical University Prague
Faculty of Electrical Engineering
Center for Machine Perception
Modifications:
31-may-2004, VF
23-jan-2003, VF