A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Evaluation of Ensemble Classifiers for Handwriting Recognition
2013
International Journal of Modern Education and Computer Science
One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed for homogeneous ensemble classifiers using bagging and heterogeneous ensemble classifiers using arcing classifier and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function
doi:10.5815/ijmecs.2013.11.02
fatcat:sj42fc3ajfcnvi3q34y2tcdhdu