Abstract
Adaboost, one of the most famous boosting algorithms, has been used in various fields of machine learning. With its success, many people focus on the improvement of this algorithm in different ways. In this paper, we propose a new algorithm to improve the performance of adaboost by the theory of multi-view learning, which is called Embedded Multi-view Adaboost (EMV-Adaboost). Different from some approaches used by other researchers, we not only blend multi-view learning into adaboost thoroughly, but also output the final hypothesis in a new form of the combination of multi-learners. These theories are combined into a whole in this paper. Furthermore, we analyze the effectiveness and feasibility of EMV-Adaboost. Experimental results with our algorithm validate its effectiveness.
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Xu, Z., Sun, S. (2010). An Algorithm on Multi-View Adaboost. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Theory and Algorithms. ICONIP 2010. Lecture Notes in Computer Science, vol 6443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17537-4_44
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DOI: https://doi.org/10.1007/978-3-642-17537-4_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17536-7
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