@article{MAKHILLJEAS2019141318034,
    title = {A New Technique for Support Vector Machine Parameters Optimization
Based on Modiefied PSO Algorithm},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {13},
    pages = {4597-4602},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.4597.4602},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.4597.4602},
    author = {Sabah Khudhair,Abdullah Aziz and},
    keywords = {Biometric,optimization,face recognition,PSO,SCface,experiments},
    abstract = {Support vector machine can determine the global finest solutions in many complicated problems and
it is widely used for human face classification in the last years. Nevertheless, one of the main limitations of SVM
is optimizing the training parameters, especially when SVM used in face recognition domains. Various
methodologies are used to deal with this issue such as PSO, OPSO, AAPSO and AOPSO. Nevertheless, there
is a room of advancements in this kind of optimization process. Lately, an improved version of PSO is
developed which is called modified PSO. In this study, a new technique based on modified PSO, called
(Modified PSO-SVM) is proposed to optimize SVM parameters. The proposed scheme utilizes modified PSO
to seek the finest parameters of SVM two human face datasets: SCface, CASIAV5 and CMU Multi-PIE face
datasets are used in the experiments. Then, a comparison is done with the PSO-SVM, OPSO-SVM and
AOPSO-SVM and it showed promising results in terms of accuracy.}
    }