F. Maire (Australia)
Kernel machines, Bayesian Point, Centroid.
The support vector machine solution corresponds to the center of the largest sphere inscribed in version space. Alternative approaches like Bayesian Point Machine (BPM) and Analytic Center Machine have suggested that the generalization performance can be further enhanced by considering other possible centers of version space like the centroid (center of mass). We present an algorithm to compute exactly the centroid of higher dimensional polyhedra, then derive approximation algorithms to build a new learning machine whose performance is comparable to BPM.
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