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In a multi-parameter learning problem, besides choosing the architecture of the learner, there is the problem of finding the optimal parameters to get maximum performance. When the number of parameters to be tuned increases, it becomes infeasible to try all the parameter sets, hence we need an automatic mechanism to find the optimum parameter setting using computationally feasible algorithms. In this...
In this work, a new approach to natural-language chunking using an evolutionary model is proposed. This uses previously captured training information to guide the evolution of the model. In addition, a multi-objective optimization strategy is used to produce the best solutions based on the internal and the external quality of chunking. Experiments and the main results obtained using the model and...
In this thesis, memory principle and ant colony algorithm are fused and applied in intrusion detection system. The method of controlling pheromone used in ant colony algorithm is applied to simulate the memory process of human brain, and the concept of pheromone is put forth. The processes of memorizing and forgetting are reasonably interpreted through the increase and decrease of pheromone, and in...
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