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We define a collection of language classes which are TxtEx-learnable (learnable in the limit from positive data). The learners map any data input to an element of a fixed lattice, and keep the least upper bound of all lattice elements thus obtained as the current hypothesis. Each element of the lattice is a grammar for a language, and the learner climbs the(More)
We provide a new term-like representation for multi-dimensional trees as defined by Rogers [8,9] which establishes them as a direct generalization of classical trees. As a consequence these structures can be used as input for finite-state applications based on classical tree language theory. Via the correspondence between string and tree languages these(More)
1 D „imo uötzing 2 1 pf s† ! e˜teilung snform—tikD …niversität „rierD SRPVT „rierD qerm—ny Abstract. „he ™l—ss of regul—r l—ngu—ges is not identi(—˜le from posiE tive d—t— in qold9s l—ngu—ge le—rning modelF w—ny —ttempts h—ve ˜een m—de to de(ne interesting ™l—sses th—t are le—rn—˜le in this modelD preferE —˜ly with the —sso™i—ted le—rner h—ving ™ert—in(More)
We present a learning algorithm for regular languages that unifies three existing ones for the settings of minimally adequate teacher learning, learning from membership queries and positive data, and learning from positive and negative data, respectively. We choose these three algorithms as an example to back up the conjecture that the learning process of(More)
Four classical kinds of information sources that are consulted in formal language learning processes as studied in the area of Grammatical Inference are the following: Membership queries, equivalence queries, finite subsets of the target language (positive samples), and finite subsets of its complement (negative samples). One of the language classes that(More)