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. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Sato, M. A., & Oba, S. (2002). Incremental sparse kernel machine. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 700–706). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_114
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