| dc.contributor.author | Kilic, Niyazi | |
| dc.contributor.author | Tartar, Ahmet | |
| dc.contributor.author | Akan, Aydin | |
| dc.date.accessioned | 2021-03-06T21:26:15Z | |
| dc.date.available | 2021-03-06T21:26:15Z | |
| dc.identifier.citation | Tartar A., Kilic N., Akan A., "Bagging Support Vector Machine Approaches for Pulmonary Nodule Detection", International Conference on Control, Decision and Information Technologies (CoDIT), Hammamet, Tunus, 6 - 08 Mayıs 2013, ss.47-50 | |
| dc.identifier.other | av_feacdaa5-12b0-4e3b-976a-ac55fdae3dbd | |
| dc.identifier.other | vv_1032021 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12627/166543 | |
| dc.identifier.uri | https://doi.org/10.1109/codit.2013.6689518 | |
| dc.description.abstract | In this paper, pulmonary nodules extracted from computed tomography (CT) images are classified by the single and bagging support vector machine (SVM) classifiers. To determine features, two dimensional principal component analysis is performed. In order to select the best features, three different models are proposed. These models are tested with classifiers of both single SVM and bagging SVM. As a result of tests, bagging SVM is shown to be superior to single SVM. | |
| dc.language.iso | eng | |
| dc.subject | MÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK | |
| dc.subject | Bilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği | |
| dc.subject | Kontrol ve Sistem Mühendisliği | |
| dc.subject | Mühendislik, Bilişim ve Teknoloji (ENG) | |
| dc.subject | Bilgisayar Bilimleri | |
| dc.subject | Algoritmalar | |
| dc.subject | Sinyal İşleme | |
| dc.subject | Mühendislik ve Teknoloji | |
| dc.subject | BİLGİSAYAR BİLİMİ, YAPAY ZEKA | |
| dc.subject | Bilgisayar Bilimi | |
| dc.subject | Mühendislik | |
| dc.subject | OTOMASYON & KONTROL SİSTEMLERİ | |
| dc.title | Bagging Support Vector Machine Approaches for Pulmonary Nodule Detection | |
| dc.type | Bildiri | |
| dc.contributor.department | İstanbul Üniversitesi , , | |
| dc.contributor.firstauthorID | 140915 | |
