Training support vector machines with particle swarms
Ulrich Paquet, Andries Petrus Engelbrecht · 2004
Training a support vector machine requires solving a constrained quadratic programming problem. Linear particle swarm optimization is intuitive and simple to implement, and is presented as an alternative to current numeric SVM training methods. Performance of the new algorithm is demonstrated on the MNIST character recognition dataset.