Selective ensemble of support vector data descriptions for novelty detection
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
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Ensemble learning is a powerful machine learning paradigm which has exhibited apparent advantages in many applications. An ensemble in the context of machine learning can be broadly defined as a machine learning system that is constructed with a set of individual models working in parallel and whose outputs are combined with a decision fusion strategy to produce a single answer for a given problem. In this paper we introduce core of ensemble learning and key techniques to improve ensemble learning. Based on this we describe the procedure of two typical algorithms, i.e., adaboost and bagging, in detail. Finally we testify the superiority in classification accuracy with some experiments.