On the pattern recognition of noisy subsequence trees

We consider the problem of recognizing ordered labeled trees by processing their noisy subsequence-trees which are "patched-up" noisy portions of their fragments. We assume that H, a finite dictionary of ordered labeled trees, is given. X* is an unknown element of H, and U is any arbitrary subsequence-tree of X*. We consider the problem of estimating X* by processing Y, which is a noisy version of U. The solution which we present is, to our knowledge, the first reported solution to the problem. We solve the problem by sequentially comparing Y with every element X of H, the basis of comparison being a new dissimilarity measure between two trees, which implicitly captures the properties of the corrupting mechanism that noisily garbles U into Y. The algorithm which incorporates this constraint has been used to test our pattern recognition system, and the experimental results obtained demonstrate good accuracy.