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An introduction to support Vector Machines: and other kernel-based learning methods
A brief history of cellular automata
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Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms)
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Cellular Automata is a dynamic system composed of very simple, uniformly interconnected cells. It provides us with an excellent platform for performing complex computations+ in a very simple way. It can be implemented as a simple distributed system taking advantage of the parallel architecture. Each cell behaves as a very simple computer machine. Cellular Automata has many applications today especially in the simulation of chaotic phenomena. It is not surprising that one rule of cellular automata is equivalent to the Turing machine. Our novel contribution in this paper is a design of a cellular automata model that can be used in the Neuroimaging field, specifically for functional magnetic resonance imaging (fMRI) brain images classification. We show that cellular automata outperform the support vector machine classification method used recently for the same purpose in terms of accuracy, sensitivity, specificity and performance. To the best of our knowledge this paper is the first to introduce the cellular automata in the Neoroimaging field.