TY - GEN
T1 - Can AdaBoost.M1 learn incrementally? A comparison to learn++ under different combination rules
AU - Mohammed, Hussein Syed
AU - Leander, James
AU - Marbach, Matthew
AU - Polikar, Robi
PY - 2006/1/1
Y1 - 2006/1/1
N2 - We had previously introduced Learn++, inspired in part by the ensemble based AdaBoost algorithm, for incrementally learning from new data, including new concept classes, without forgetting what had been previously learned. In this effort, we compare the incremental learning performance of Learn++ and AdaBoost under several combination schemes, including their native, weighted majority voting. We show on several databases that changing AdaBoost's distribution update rule from hypothesis based update to ensemble based update allows significantly more efficient incremental learning ability, regardless of the combination rule used to combine the classifiers.
AB - We had previously introduced Learn++, inspired in part by the ensemble based AdaBoost algorithm, for incrementally learning from new data, including new concept classes, without forgetting what had been previously learned. In this effort, we compare the incremental learning performance of Learn++ and AdaBoost under several combination schemes, including their native, weighted majority voting. We show on several databases that changing AdaBoost's distribution update rule from hypothesis based update to ensemble based update allows significantly more efficient incremental learning ability, regardless of the combination rule used to combine the classifiers.
UR - http://www.scopus.com/inward/record.url?scp=33749842512&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33749842512&partnerID=8YFLogxK
M3 - Conference contribution
SN - 3540386254
SN - 9783540386254
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 254
EP - 263
BT - Artificial Neural Networks, ICANN 2006 - 16th International Conference, Proceedings
PB - Springer Verlag
T2 - 16th International Conference on Artificial Neural Networks, ICANN 2006
Y2 - 10 September 2006 through 14 September 2006
ER -