Playing billiards in version space
Neural Computation
The Journal of Machine Learning Research
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The Support Vector Machine (SVM) solution correspondsto the centre of the largest sphere inscribed in versionspace. Alternative approaches like Bayesian PointMachines (BPM) and Analytic Centre Machines have suggestedthat the generalization performance can be furtherenhanced by considering other possible centres of versionspace like the centroid (centre of mass) or the analytic centre.We present an algorithm to compute exactly the centroidof higher dimensional polyhedra, then derive approximationalgorithms to build a new learning machine whoseperformance is comparable to BPM. We also show that forregular kernel matrices (Gaussian kernels for example), theSVM solution can be obtained by solving a linear system ofequalities.