TY - GEN
T1 - Experimental comparison between bagging and Monte Carlo ensemble classification
AU - Esposito, Roberto
AU - Saitta, Lorenza
PY - 2005
Y1 - 2005
N2 - Properties of ensemble classification can be studied using the framework of Monte Carlo stochastic algorithms. Within this frame-work it is also possible to define a new ensemble classifier, whose accuracy probability distribution can be computed exactly. This paper has two goals: first, an experimental comparison between the theoretical predictions and experimental results; second, a systematic comparison between bagging and Monte Carlo ensemble classification.
AB - Properties of ensemble classification can be studied using the framework of Monte Carlo stochastic algorithms. Within this frame-work it is also possible to define a new ensemble classifier, whose accuracy probability distribution can be computed exactly. This paper has two goals: first, an experimental comparison between the theoretical predictions and experimental results; second, a systematic comparison between bagging and Monte Carlo ensemble classification.
UR - http://www.scopus.com/inward/record.url?scp=31844451707&partnerID=8YFLogxK
U2 - 10.1145/1102351.1102378
DO - 10.1145/1102351.1102378
M3 - Conference contribution
AN - SCOPUS:31844451707
SN - 1595931805
T3 - ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning
SP - 209
EP - 216
BT - ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning
A2 - Raedt, L.
A2 - Wrobel, S.
T2 - ICML 2005: 22nd International Conference on Machine Learning
Y2 - 7 August 2005 through 11 August 2005
ER -