Anna Kasprzik

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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)
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 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 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)