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Combining several classifiers has become a very active subdiscipline in the field of pattern recognition. For years, pattern recognition community has focused on seeking optimal learning algorithms able to produce very accurate classifiers. However, empiri ...
This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement lear ...
This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement lear ...
This paper introduces a discriminative model for the retrieval of images from text queries. Our approach formalizes the retrieval task as a ranking problem, and introduces a learning procedure optimizing a criterion related to the ranking performance. The ...
Biometric authentication is a process of verifying an identity claim using a person's behavioral and physiological characteristics. Due to vulnerability of the system to environmental noise and variation caused by the user, fusion of several biometric-enab ...
Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of using an adaptive policy for training and combining the base classifiers is put ...
Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...
Recent advances in the field of Brain-Computer Interfaces (BCIs) have shown that BCIs have the potential to provide a powerful new channel of communication, completely independent of muscular and nervous systems. However, while there have been successful l ...
Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper we attempt to train and combine the base classifiers using an adaptive policy. This policy is ...
Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...
