| |
Reduced set construction using Fixpoint method by Schölkopf et al.
|
a = rsc_fp(alg,hyper)
generates a rsc object, using the fixpoint method
hyperparameters:
child=svm algorithm worked on
rsv=.5 if rsv e [0,1): #rsv = #sv * rsv
if rsv > 1: #rsv
max_it=10 maximum of iterations per class
eps=1e-5 epsilon
model:
alpha new alphas for rs-vectors
Xsv rs vectors
stats:
w2=0 final value of ||w-w*||^2
dw=0 total decrease in ||w-w*||^2
methods:
train constructs a reduced set, returns trained rs-machine
test tests new rs-machine on supplied data
remark:
supports only rbf-kernels!
example:
d=gen(toy2d('2circles','l=100'));
[r,a]=train(svm({kernel('rbf',1),'C=10000','alpha_cutoff=1e-2'}),d);
[r,a2]=train(rsc_fp(a,'rsv=.3'),d);
test(a2,d,loss)
author: b. schoelkopf et al.
reference: learning with kernels, chpt. 18