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(Agnostic) PAC Learning Concepts in Higher-Order Logic

  • K. S. Ng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4212)

Abstract

This paper studies the PAC and agnostic PAC learnability of some standard function classes in the learning in higher-order logic setting introduced by Lloyd et al. In particular, it is shown that the similarity between learning in higher-order logic and traditional attribute-value learning allows many results from computational learning theory to be ‘ported’ to the logical setting with ease. As a direct consequence, a number of non-trivial results in the higher-order setting can be established with straightforward proofs. Our satisfyingly simple analysis provides another case for a more in-depth study and wider uptake of the proposed higher-order logic approach to symbolic machine learning.

Keywords

Vertex Cover Inductive Logic Programming Logical Setting Computational Learning Theory Predicate Class 
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 2006

Authors and Affiliations

  • K. S. Ng
    • 1
  1. 1.Symbolic Machine Learning and Knowledge Acquisition, National ICT Australia Limited 

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