dogma

  DOGMA是一个关于在线学习的工具箱，里面很多算法十分实用，包括OISVM(online independent SVM).(DOGMA is a useful online learning alogrithm by matlab code. It includes a lot of useful alogrithm, including Online independent SVM(OISVM))

banditron_multi_train.m,2703,2010-05-03
chisquare_sparse.c,2574,2010-05-03
chisquare_sparse.mexa64,8557,2010-02-18
compute_kernel.m,5106,2010-05-03
Contents.m,2755,2010-06-11
demo.m,7220,2010-05-03
demos
.....\.directory,50,2010-06-11
.....\data
.....\obscure
.....\.......\demo_obscure.m,3706,2010-06-10
.....\.......\obscure_test.m,1659,2010-06-10
.....\.......\htm" target=_blank>README,955,2010-06-09
.....\om-2
.....\....\demo_om2.m,1228,2010-06-11
.....\....\htm" target=_blank>README,896,2010-06-11
.....\omcl
.....\....\demo_omcl.m,1734,2010-06-10
.....\....\htm" target=_blank>README,900,2010-06-11
hist_intersection_sparse.c,2606,2010-05-03
hist_intersection_sparse.mexa64,8565,2010-02-18
kbeta_double.cpp,4482,2010-06-09
kbeta_double.mexa64,8696,2010-04-02
kbeta_single.cpp,4714,2010-06-09
kbeta_single.mexa64,12879,2010-04-02
k_alma2_train.m,4412,2010-05-03
k_forgetron_st_train.m,3851,2010-05-03
k_obscure_batch_train.m,7782,2010-05-03
k_obscure_online_train.m,6394,2010-06-10
k_obscure_train.m,2611,2010-05-03
k_oisvm_train.m,7163,2010-05-03
k_om2_mp_multi_train.m,5618,2010-06-10
k_om2_multi_train.m,5063,2010-06-10
k_omcl_multi_train.m,15554,2010-06-08
k_pa_multi_train.m,5416,2010-05-03
k_pa_train.m,3616,2010-05-03
k_pegasos_train.m,3926,2010-05-03
k_perceptron_multi_train.m,4694,2010-05-03
k_perceptron_train.m,3884,2010-05-03
k_projectron2_multi_train.m,7267,2010-05-03
k_projectron2_train.m,4394,2010-05-03
k_projectron_train.m,3766,2010-05-03
k_sop_train.m,3588,2010-05-03
k_ssmd_train.m,3980,2010-05-03
licence.txt,35147,2010-01-15
model_init.m,1504,2010-05-03
model_mc_init.m,1307,2010-05-03
model_predict.m,4714,2010-06-10
pa_multi_train.m,3554,2010-05-03
pa_train.m,3193,2010-05-03
perceptron_train.m,2483,2010-05-03
pnorm_train.m,2886,2010-05-03
randnorm.m,1625,2010-05-03
readme.txt,4124,2010-06-11
shuffledata.m,1042,2010-05-03
sop_adapt_train.m,2875,2010-05-03
sop_train.m,2848,2010-06-04