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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 generalize a learning algorithm by Drewes and Högberg [1] for regular tree languages based on a learning model proposed by Angluin [2] to recognizable tree languages of arbitrarily many dimensions, so-called multi-dimensional trees. Multi-dimensional trees over multi-dimensional tree domains have been defined by Rogers [3, 4]. However, since the… (More)

In this paper two methods of how to make derivation in a Tree Adjoining Grammar a regular process without loss of expressive power are presented and compared. In a TAG, derivation is based upon the expansion of tree nodes into other trees. One regularization method is based on an algebraic operation called Lifting, while the other exploits an additional… (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)

We define a two-step learner for RFSAs based on an observation table by using an algorithm for minimal DFAs to build a table for the reversal of the language in question and showing that we can derive the minimal RFSA from it after some simple modifications. We compare the algorithm to two other table-based ones of which one (by Bollig et al. [8]) infers a… (More)