Compact multi-class support vector machine

Multiclass support vector machines (MSVMs) have become a very appealing machine learning approach due to their good results in many classification problems. The resulting machines, though, are usually too large for being usable in many real world applications, especially when fast real-time response is needed. Some approaches aim at decreasing the complexity of the resulting full-size SVM classifiers. The most successful methods follow the “reduced-set” procedure, but they have to start from a full SVM solution, and then solve a pre-image problem, prone to fall in local minima. We propose here a compact multiclass SVM (CMSVM) method, that does not need the full SVM solution as a starting point (and hence scales potentially better), and does not need to address the pre-image problem. We evaluate the performance of the proposed scheme by means of real world data sets, and we compare it against other state-of-the-art MSVM techniques.