Communications of the ACM
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
Machine Learning
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
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Boosting-based Transductive Learning for Text Detection
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Evolutionary discriminant analysis
IEEE Transactions on Evolutionary Computation
Combinations of weak classifiers
IEEE Transactions on Neural Networks
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This paper describes a new approach using particle swarm optimisation (PSO) within AdaBoost for object detection. Instead of using the time consuming exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we propose two PSO based methods in this paper. The first uses PSO to evolve and select the good features only and the weak classifiers use a kind of decision stump. The second uses PSO for both selecting the good features and evolving weak classifiers in parallel. These two methods are examined and compared on a pasta detection data set. The experiment results show that both approaches perform quite well for the pasta detection problem, and that using PSO for selecting good individual features and evolving associated weak classifiers in AdaBoost is more effective than for selecting features only for this problem.