Data Classification Using Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron

Chapter 2 presents an approach to perform large-scale data classification using Support Vector Machines (SVM), trained using Kernel-Adatron (KA) algorithm, combined with Artificial Bee Colony (ABC), micro-Artificial Bee Colony ( μ ABC), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The combination of KA and bio-inspired algorithms allows us to obtain a parallelized system of classification that may be effectively applied to solve pattern recognition with data of large dimension, keeping its computational complexity lower than previous large scale classifiers that use SVM. Our proposed SVM-bio-inspired algorithm can be used to solve problems, such as classification of chromosomes, spam filtering, information security, and others, where the dimension and/or amount of data is very large, and where the generalization of knowledge is highly desirable for obtaining a low training error.

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