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Since their early development, genetic programming-based algorithms have been showing to be successful at challenging problems, attaining several human-competitive results and other awards. This paper will present another achievement of such algorithms by describing how our team has won an international machine-learning competition. We have solved, by means of grammar-based genetic programming techniques,...
This paper suggests an approach to develop a class of evolving neural fuzzy networks with adaptive feature selection. The approach uses the neo-fuzzy neuron structure in conjunction with an incremental learning scheme that, simultaneously, selects the input variables, evolves the network structure, and updates the neural network weights. The mechanism of the adaptive feature selection uses statistical...
Recently, Bayesian networks became a popular technique to represent knowledge about uncertain domains and have been successfully used for applications in various areas. Even though there are several cases of success and Bayesian networks have been proved to be capable of representing uncertainty in many different domains, there are still two significant barriers to build large-scale Bayesian networks:...
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