Abstract
The Relevance Vector Machine (RVM) gives a probabilistic model for a sparse kernel representation. It achieves comparable performance to the Support Vector Machine (SVM) while using substantially fewer kernel bases. However, the computational complexity of the RVM in the training phase prohibits its application to large datasets. In order to overcome this difficulty, we propose an incremental Bayesian method for the RVM. The preliminary experiments showed the efficiency of our method for large datasets.
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Sato, Ma., Oba, S. (2002). Incremental Sparse Kernel Machine. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_114
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DOI: https://doi.org/10.1007/3-540-46084-5_114
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