@inproceedings{ouyed_feature_2014,

title = {Feature relevance for kernel logistic regression and application to action classification},

author = {O. Ouyed and M. S. Allili},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919884283&doi=10.1109%2fICPR.2014.237&partnerID=40&md5=616038cd7924614fd633061dfed32903},

doi = {10.1109/ICPR.2014.237},

isbn = {978-1-4799-5208-3},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings - International Conference on Pattern Recognition},

pages = {1325–1329},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {An approach is proposed for incorporating feature relevance in mutinomial kernel logistic regression (MKLR) for classification. MKLR is a supervised classification method designed for separating classes with non-linear boundaries. However, it assumes all features are equally important, which may decrease classification performance when dealing with high-dimensional or noisy data. We propose a feature weighting algorithm for MKLR which automatically tunes features contribution according to their relevance for classification and reduces data over-fitting. The proposed algorithm produces more interpretable models and is more generalizable than MKLR, Kernel-SVM and LASSO methods. Application to simulated data and video action classification has provided very promising results compared to the aforementioned classification methods. © 2014 IEEE.},

note = {ISSN: 10514651},

keywords = {Action classifications, Action recognition, Classification (of information), Classification methods, Classification performance, Feature relevance, Kernel logistic regression, Logistic regression, Multinomial kernels, Pattern Recognition, Supervised classification, Support vector machines},

pubstate = {published},

tppubtype = {inproceedings}

}