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The main property of kernel methods is that they can implicitly perform a nonlinear mapping of the input data into a high-dimensional space. This mapping allows to find a simpler structure within space without increasing the number of parameters increasing the clustering quality. Therefore, kernel methods may find better results for data arranged not linearly. Many methods presented in the literature...
The future Internet scenario consists of a higher number of users and applications, which demand more resources from the communication infrastructure. Techniques for providing performance and scalability, such as Traffic Engineering (TE), will always be necessary even if the transmission rate is very high, because of such demands. Quality of Service is one of the solutions that can be used to improve...
Kernel clustering methods have been very important in application of non-supervised machine learning to real problems. Kernel methods possess many advantages other than non-linearity such as modularity, ability to work with heterogeneous descriptions of data, incorporation of prior knowledge etc. In this paper, we present a clustering method based on kernel functions for partitioning a set of interval-valued...
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