A Secure and Auditable Cryptographic-Based e-Voting Scheme

2015 ◽  
Author(s):  
Mona F. M. Mursi ◽  
Ghazy M. R. Assassa ◽  
Ahmed A. Abdelhafez ◽  
Kareem M. Abosamra
Keyword(s):  
Voting Scheme

Next-Generation Relay Voting Scheme Design Leveraging Consensus Algorithms

2021 ◽  
Author(s):  
Nicholas Jacobs ◽  
Adam Summers ◽  
Shamina Hossain-McKenzie ◽  
Daniel Calzada ◽  
Hanyue Li ◽  
...  
Keyword(s):  
Next Generation ◽  
Consensus Algorithms ◽  
Scheme Design ◽  
Voting Scheme

A NEW ENSEMBLE LEARNING ALGORITHM USING REGIONAL CLASSIFIERS

2013 ◽  
Vol 22 (04) ◽  
pp. 1350025 ◽  
Author(s):  
BYUNGWOO LEE ◽  
SUNGHA CHOI ◽  
BYONGHWA OH ◽  
JIHOON YANG ◽  
SUNGYONG PARK
Keyword(s):  
Ensemble Learning ◽  
Learning Algorithm ◽  
Ensemble Methods ◽  
Feature Space ◽  
Training Data ◽  
Learning Method ◽  
Weighted Voting ◽  
Ensemble Learning Algorithm ◽  
Voting Scheme ◽  
Base Learner

We present a new ensemble learning method that employs a set of regional classifiers, each of which learns to handle a subset of the training data. We split the training data and generate classifiers for different regions in the feature space. When classifying an instance, we apply a weighted voting scheme among the classifiers that include the instance in their region. We used 11 datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as RBE, bagging and Adaboost. As a result, we found that the performance of our method is comparable to that of Adaboost and bagging when the base learner is C4.5. In the remaining cases, our method outperformed other approaches.

An iterative multi-scale tensor voting scheme for perceptual grouping of natural shapes in cluttered backgrounds

2009 ◽  
Vol 113 (1) ◽  
pp. 126-149 ◽  
Author(s):  
Leandro Loss ◽  
George Bebis ◽  
Mircea Nicolescu ◽  
Alexei Skurikhin
Keyword(s):  
Perceptual Grouping ◽  
Tensor Voting ◽  
Multi Scale ◽  
Voting Scheme
Sign in / Sign up

Export Citation Format

Share Document