Machine Learning on the Basis of Formal Concept Analysis

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.