In this paper, we study the error analysis of indefinite kernel network with coefficient regularization for non-iid sampling. The framework under investigation is different from classical kernel learning. The kernel function satisfies only the continuity and uniform boundedness; the standard bound assumption for output data is abandoned and we carry out the error analysis with output sample values satisfying a generalized moment hypothesis. Satisfactory error bounds and learning rates independent of capacity are derived by the techniques of integral operator for this learning algorithm.

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