Anna Kasprzik

Learn More
Wedefine 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)
The class of regular languages is not identi able from positive data in Gold's language learning model. Many attempts have been made to de ne interesting classes that are learnable in this model, preferably with the associated learner having certain advantageous properties. Heinz '09 presents a set of language classes called String Extension (Learning)(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)
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)