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Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, though their drawback is the presence of metaparameters. The metaparameters are typically supposed to be given by an user. In this paper, we develop a method for finding optimal values for metaparameters, namely type of kernel function, kernel’s parameter and regularization...
Metalearning deals with an important problem in machine-learning, namely selecting the right techniques to model the data at hand. In most of the metalearning approaches, a notion of similarity between datasets is needed. Our approach derives the similarity measure by combining arbitrary attribute similarity functions ordered by the optimal attribute assignment. In this paper, we propose a genetic...
With the growing amount of data available in today's world, the emphasis is laid on the automatic configuration of data analysis - metal earning. This paper elaborates one of the metal earning sub problems, the data mining method recommendation. Based on a metric over the data features called metadata, we have proposed a solution exploiting clustering of datasets. The agglomerative algorithm is used...
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