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21. W. J. L. Seitz, Deadlock-free message routing in multiprocessor interconnection networks, IEEE Trans. , C-36: 547–553, 1987. 23. M. , Strategies for the implementation of interconnection network simulators on parallel computers, Int. J. Comput. Syst. Sci. , 13 (1): 5–16, 1998. 564 INTERFACE DESIGN Reading List Books That Cover Interconnection Networks J. Duato, S. Yalamanchili, and L. Ni, Interconnection Networks: An Engineering Approach, Los Alamitos, CA: IEEE Computer Society Press, 1997.
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1 for an excellent introduction) can be used for many supervised learning tasks. Given as training data N input/target data vector pairs D ϭ ͕xn, tn͖ (a mapping from input to target data), with dimensions M and K, respectively, the aim of a supervised learning process is to learn how to predict output data given new input data, which is written as a K-dimensional function yk(xn;w) depending on the current input vector x and the NN parameter value vector w with W weights. The weights are combined in structures, whose different types are discussed in more detail below.