Predicate calculus feature generation

  • David Rothenberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 22)


A summary description (proof of theorems omitted): An adaptive pattern representation and recognition strategy for application to mechanized interpretation of (sampled) pictorial data (other applications are appropriate) is described. The system generates its own features which are formulae in a subset of the weak (in the sense that only quantification over finite sets is permitted) second order predicate calculus. The models of such formulae define the "objects" in a description of the data, which is hierarchical both with respect to features and extensions. The hierarchy is automatically constructed, thereby implementing changes in "problem representation". Relations between the syntax and semantics of formulae in the weak second order predicate calculus are derived (by extending the syntax of the calculus) and utilized. Minimal use is made of the finiteness of the input data by the methods employed. That is, in pictorial pattern recognitions, the adaptive feature generation (i.e., "learning") algorithms are independent of the fineness of grain of the sampling of an input picture. Because this approach is used, many difficult problems of a purely mathematical nature acquire practical importance. Computation is reduced through the use of topological methods and the system is at present in a stage of development appropriate for programming and use in a variety of practical applications.


Distributive Form Universal Quantifier Neutral Mutation Existential Quantifier Frechet Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1975

Authors and Affiliations

  • David Rothenberg
    • 1
  1. 1.University College, Rutgers UniversityUSA

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