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Symbolic Indirect Correlation (SIC) is a nonparametric method that offers significant advantages for recognition of ordered unsegmented signals. A previously introduced formulation of SIC based on subgraph-isomorphism requires very large reference sets in the presence of noise. In this paper, we seek to address this issue by formulating SIC classification as a maximum likelihood problem. We present experimental evidence that demonstrates that this new approach is more robust for the problem of online handwriting recognition using noisy input.