Can adaboost.m1 learn incrementally? a comparison to learn++ under different combination rules

Authors:
Hussein Syed Mohammed;James Leander;Matthew Marbach;Robi Polikar
Affiliations:
Electrical and Computer Engineering, Rowan University, Glassboro, NJ;Electrical and Computer Engineering, Rowan University, Glassboro, NJ;Electrical and Computer Engineering, Rowan University, Glassboro, NJ;Electrical and Computer Engineering, Rowan University, Glassboro, NJ
Venue:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

Citing 4
Cited 1

A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
On Combining Classifiers

IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Learn++: an incremental learning algorithm for supervised neuralnetworks

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Hybrid active learning for reducing the annotation effort of operators in classification systems

Pattern Recognition

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Abstract

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.