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.

Multi-prototype Support Vector Machines

AIOLLI, FABIO;SPERDUTI, ALESSANDRO
2003

Abstract

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.
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence
0127056610
9780127056616
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/2462629
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