Multi-prototype Support Vector Machine

We extend multiclass SVM to multiple prototypes per class. For this framework, we give a compact constrained quadratic problem and we suggest an efficient algorithm for its optimization that guarantees a local minimum of the objective function. An annealed process is also proposed that helps to escape from local minima. Finally, we report experiments where the performance obtained using linear models is almost comparable to that obtained by state-of-art kernel-based methods but with a significant reduction (of one or two orders) in response time.

• Type: Conference
• Year: 2003
• Where: IJCAI
• Authors: Fabio Aiolli, Alessandro Sperduti
• Keywords: Constrained Quadratic Problem, IJCAI 2003, IJCAI 2007, Multiclass Svm, State-of-art Kernel-based Methods