Fast Training of Multi-Class Support Vector Machines

In this paper, we introduce a new approach to using binary support vector machines for multi-class object recognition. The proposed method is a hierarchical classifier utilizing hypercubes and informed search to coarsely partition the input space. Results are shown using a dataset of 100 objects. The method is compared with the one-against-one approach. Training times are shown to be significantly reduced, while test performance is comparable.