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These include the following: Rebuilding known grammars. Using a pre-defined grammar, unstructured data is generated. Based on this data, the GI system tries to induce the original grammar. The problem here is that for most languages, there is more than one grammar that can be used to describe that language. Although all regular grammars can be transformed into a canonical form, no such canonical form exists for context-free grammars. Therefore there is no automated way to determine that the language described by a grammar submitted by a competitor and the target grammar are the same.

Portions of this training set are then systematically removed and the two learning algorithms are then applied on the new test sets. With this method we can check which algorithm requires the least amount of training strings in order to infer a good DFA. Since the target DFA is not known, training sets with 30,000 strings are initially used to exactly infer the target DFAs. The graphs of figure 4 show the average classification rates for ten DFAs with target sizes of 256, 192 and 128 states respectively.

E. the sequence of states) ending up to a particular position of the amino acid sequence (the sequence of symbols), by Each state k is associated with an emission probability which is the probability of a particular symbol to be emitted by that state. When using labeled sequences, each amino acid sequence x is accompanied by a sequence of labels y for each position i in the sequence: Consequently, one has to declare a new probability distribution, in addition to the transition and emission probabilities, the probability of a state k having a label c.

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