vote classification
Recently Published Documents


TOTAL DOCUMENTS

11
(FIVE YEARS 2)

H-INDEX

5
(FIVE YEARS 0)

2021 ◽  
Vol 17 (2) ◽  
pp. 104-109
Author(s):  
Muge Ozcelik Korkmaz ◽  
◽  
Mehmet Guven ◽  
Halil Elden ◽  
Mamut Sinan Yilmaz ◽  
...  

Author(s):  
Eric J. Kezirian ◽  
Madeline J. L. Ravesloot ◽  
Winfried Hohenhorst ◽  
Nico de Vries

Drug-induced sleep endoscopy (DISE) is an upper airway evaluation technique in which fiberoptic examination is performed under conditions of unconscious sedation. Unique information obtained from this three-dimensional examination of the airway potentially provides additive benefits to other evaluation methods to guide treatment selection. This chapter presents recommendations regarding DISE technique. It presents the Velum, Oropharynx, Tongue Base, Epiglottis (VOTE) classification for reporting DISE findings, which incorporates the four major structures that contribute to airway obstruction in most patients. The authors review the evidence concerning DISE test characteristics and the association between DISE findings and treatment outcomes, including for upper airway stimulation.


2019 ◽  
Author(s):  
Piush Aggarwal ◽  
Tobias Horsmann ◽  
Michael Wojatzki ◽  
Torsten Zesch
Keyword(s):  

2016 ◽  
Vol 174 ◽  
pp. 344-351 ◽  
Author(s):  
Maite Termenon ◽  
Manuel Graña ◽  
Alexandre Savio ◽  
Anton Akusok ◽  
Yoan Miche ◽  
...  

2012 ◽  
Vol 263-266 ◽  
pp. 2561-2565
Author(s):  
Li Jun Shi ◽  
Xian Cheng Mao ◽  
Zheng Lin Peng

This paper presents a new method for classification of remote sensing image based on multiple classifiers combination. In this method, three supervised classifications such as Mahalanobis Distance, Maximum Likelihood and SVM are selected to sever as the sub-classifications. The simple vote classification, maximum probability category method and fuzzy integral method are combined together according to certain rules. And adopted color infrared aerial images of Huairen country as the experimental object. The results show that the overall classification accuracy was improved by 12% and Kappa coefficient was increased by 0.12 compared with SVM classification which has the highest accuracy in single sub-classifications.


2011 ◽  
Vol 268 (8) ◽  
pp. 1233-1236 ◽  
Author(s):  
Eric J. Kezirian ◽  
Winfried Hohenhorst ◽  
Nico de Vries

Sign in / Sign up

Export Citation Format

Share Document