2014
DOI: 10.15439/2014f111
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Bronchopulmonary Dysplasia Prediction Using Support Vector Machine and LIBSVM

Abstract: Abstract-The paper presents BPD (Bronchopulmonary Dysplasia) prediction for extremely premature infants after their first week of life. SVM (Support Vector Machine) algorithm implemented in LIBSVM[1] was used as classifier. Results are compared to others gathered in previous work [2] where LR (Logit Regression) and Matlab environment SVM implementation were used. Fourteen different risk factor parameters were considered and due to the high computational complexity only 3375 random combinations were analysed. C… Show more

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Cited by 4 publications
(1 citation statement)
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“…One of the most effective representatives of the shallow neural networks are support vector machines (SVM) [9], [10], [11], [12], [13]. The tuning of support vector machines is provided by using both lazy learning (the activation functions' centers tuning) and optimization procedures (the synaptic weights tuning).…”
Section: Introductionmentioning
confidence: 99%
“…One of the most effective representatives of the shallow neural networks are support vector machines (SVM) [9], [10], [11], [12], [13]. The tuning of support vector machines is provided by using both lazy learning (the activation functions' centers tuning) and optimization procedures (the synaptic weights tuning).…”
Section: Introductionmentioning
confidence: 99%