Machine Learning on the Basis of Formal Concept Analysis View Full Text


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Article Info

DATE

2001-10

AUTHORS

S. O. Kuznetsov

ABSTRACT

A model of machine learning from positive and negative examples (JSM-learning) is described in terms of Formal Concept Analysis (FCA). Graph-theoretical and lattice-theoretical interpretations of hypotheses and classifications resulting in the learning are proposed. Hypotheses and classifications are compared with other objects from domains of data analysis and artificial intelligence: implications in FCA, functional dependencies in the theory of relational data bases, abduction models, version spaces, and decision trees. Results about algorithmic complexity of various problems related to the generation of formal concepts, hypotheses, classifications, and implications. More... »

PAGES

1543-1564

References to SciGraph publications

  • 2000. Formalizing Hypotheses with Concepts in CONCEPTUAL STRUCTURES: LOGICAL, LINGUISTIC, AND COMPUTATIONAL ISSUES
  • 1983. Learning from Observation: Conceptual Clustering in MACHINE LEARNING
  • 1994-10. Generalizing version spaces in MACHINE LEARNING
  • 2001-07-12. Pattern Structures and Their Projections in CONCEPTUAL STRUCTURES: BROADENING THE BASE
  • 2000. Knowledge Discovery from Very Large Databases Using Frequent Concept Lattices in MACHINE LEARNING: ECML 2000
  • 1982. Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts in ORDERED SETS
  • 1996-06. Mathematical aspects of concept analysis in JOURNAL OF MATHEMATICAL SCIENCES
  • 1991-09. Finding all closed sets: A general approach in ORDER
  • 1998. Stepwise construction of the Dedekind-MacNeille completion in CONCEPTUAL STRUCTURES: THEORY, TOOLS AND APPLICATIONS
  • 1986-03. Induction of decision trees in MACHINE LEARNING
  • 1998. Conceptual Knowledge Discovery in Databases using formal concept analysis methods in PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1023/a:1012435612567

    DOI

    http://dx.doi.org/10.1023/a:1012435612567

    DIMENSIONS

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