All-at-Once Multiclass SVM approach for Ordinal Data
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genAllResults
libsvm_binaries
mexclp
oSVM
results
svm_binary
svm_multiclass
.gitattributes
LICENSE
OrdinalClassificationIndex.m
README.md
calc_perf.m
carsdata_parser.m
check_svm_options.m
concatenate_res.m
contingency_table.m
divide_data.m
gatherResults.m
genDataParse.m
kendalltaub.m
kendalltaub2.m
loadDataSets.m
main.m
main_run.m
myMetricSA.m
my_svm_dual_test.m
my_svm_dual_train.m
my_svm_kernelfunction.m
myplot.m
optimize.m
parse.m
plot_features.m
rint.m
std_multiclass_basic.m
std_multiclass_basic_ext.m
std_multiclass_sophisticated.m
testCreateArtificialData.m
testCreateArtificialData2.m
unimodal_basic.m
unimodal_basic_other.m
unimodal_basic_special.m
unimodal_sophisticated.m

README.md

OrdinalMulticlassSVM

Support Vector Machines (SVMs) were initially proposed to solve problems with two classes. Despite the myriad of schemes for multiclassification with SVMs proposed since then, little work has been done for the case where the classes are ordered. Mmost of the techniques presented so far in the literature can generate ambiguous regions.

All-at-Once methods have been proposed to solve this issue. A new SVM methodology based on the unimodal paradigm with the All-at-Once scheme for the ordinal classification.

http://rsousa.org

Reference

If you use this code, please cite the following article:

@inproceedings{JFCostaICMLA2010,
  author = {da Costa, Joaquim Pinto and Sousa, Ricardo and Cardoso, Jaime S},
  booktitle = {Proceedings of The Ninth International Conference on Machine Learning and Applications (ICMLA 2010)},
  title = {{An all-at-once Unimodal SVM Approach for Ordinal Classification}},
  url = {http://rsousa.org/docs/pubs/JFCostaICMLA2010.pdf},
  year = {2010}
}