Vladimir Pericliev

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When a dataset involves multiple classes, there is often a need to express the key contrasting features among these classes in humanly understandable terms, that is, to proole the classes. Commonly, one class is contrasted from the rest by aggregating the latter into a pseudo-class; alternatively, classes are treated separately without coordinating their(More)
The paper describes a novel computational tool for multiple concept learning. Unlike previous approaches, whose major goal is prediction on unseen instances rather than the legibility of the output, our MPD (Maximally Parsimonious Discrimination) program emphasizes the conciseness and intelligibility of the resultant class descriptions, using three(More)
In a companion paper ([14]), I describe UNIVAUTO (UNI-Versals AUthoring TOol), a linguistic discovery program that uncovers language universals and can write a report in English on its discoveries. In this contribution, the system is evaluated along a number of parameters that have been suggested in the literature as necessary ingredients of a successful(More)
A system is descril)ed which learns fl'om examples the Linear Precedence rules in an Immediate Dominance/Linear Precedence grammar. Given a particular hn-mediate Dominance grammar and hierarchies of feature values potentially rel evant for linearization (=the systelu's bias), the leanler generates appropriate naturM language expressions to be ewd-uated as(More)