TR98-069 Authors: RĂ¼diger Reischuk, Thomas Zeugmann

Publication: 8th December 1998 09:44

Downloads: 1113

Keywords:

A new algorithm for learning one-variable pattern languages from positive data

is proposed and analyzed with respect to its average-case behavior.

We consider the total learning time that takes into account all

operations till convergence to a correct hypothesis is achieved.

For almost all meaningful distributions

defining how the pattern variable is replaced by a string to generate

random examples of the target pattern language,

it is shown that this algorithm converges within an expected

constant number of rounds and a total learning time that is linear in the

pattern length. Thus, our solution is average-case optimal in a strong sense.

Though one-variable pattern languages can neither be finitely inferred

from positive data nor PAC-learned,

our approach can also be extended to a probabilistic finite learner

that it exactly infers all one-variable pattern languages from positive

data with high confidence.

It is a long standing open problem whether pattern languages

can be learned in case that substitutions of pattern variables by

the empty string can also occur.

Our learning strategy can be generalized to this situation as well.

Finally, we show some experimental results for the behaviour

of this new learning algorithm in pratice.