Optimização de métodos de núcleo utilizando algoritimos de enxame

Os métodos de núcleo, que incluem as máquinas de vectores de suporte, têm obtido sucesso assinalável em inúmeras áreas de aplicação. Este sucesso está, no entanto, dependente do núcleo e parâmetros definidos para cada problema. Estas questões têm sido abordadas utilizando diversas técnicas analíticas, algébricas, heurísticas e, mais recentemente, vários métodos evolucionários. Nesta dissertação apresentamos uma abordagem baseada em inteligência de enxame à optimização das máquinas de vectores de suporte. As contribuições deste trabalho incluem a análise de anteriores abordagens evolucionárias; a proposta de um algoritmo de enxame para treino de máquinas de vectores de suporte, superior à melhor abordagem evolucionária conhecida e aos métodos clássicos quando são utilizados núcleos indefinidos; e a apresentação de um algoritmo de enxame capaz de optimizar simultaneamente vários aspectos da abordagem, reduzindo substancialmente a intervenção do utilizador. Adicionalmente, desenvolvemos novos algoritmos e metodologias de utilidade genérica na área da inteligência de enxame; ### Optimizing Kernel Methods Using Swarm Intelligence Algorithms Abstract: Kernel methods, which include support vector machines, have achieved remarkable success in many application areas. This success is, however, dependent on the kernel and parameters defined for each problem. These issues have been addressed using various analytical, algebraic and heuristic techniques. More recently, various evolutionary methods have also been used. In this thesis we present an approach based on swarm intelligence for the optimization of support vector machines. The contributions of this work include the analysis of previous evolutionary approaches; the proposal of a swarm algorithm to train support vector machines that presents better performance than the best previously known evolutionary approach and can overcome the classical methods when indefinite kernels are used; and the presentation of a swarm algorithm able to simultaneously optimize several aspects of the approach, substantially reducing user intervention. Additionally, we developed new algorithms and methodologies of general utility in the area of swarm intelligence

Using a scouting predator-prey optimizer to train support vector machines with non psd kernels

In this paper, we investigate the use of an heterogeneous particle swarm optimizer, the scouting predator-prey optimizer, to train support vector machines with non positive definite kernels, including distance substitution based kernels. These kernels can arise in practical applications, resulting in multi-modal optimization problems where tra- ditional algorithms can struggle to find the global optimum. We compare the scouting predator-prey algorithm with the previous best evolutionary approach to this problem and a standard quadratic programming based algorithm, on a large set of of benchmark problems, using various non positive definite kernels. The use of cooperating scout particles allows the proposed algorithm to be more efficient than the other evolutionary approach, which is based on an evolution strategy. Both are shown to perform better than the standard algorithm in several dataset/kernel in- stances, a result that underlines the usefulness of evolutionary training algorithms for support vector machines