Ordinal mind change complexity of language identification
The approach of ordinal mind change complexity, introduced by Freivalds and Smith, uses (notations for) constructive ordinals to bound the number of mind changes made by a learning machine. This approach provides a measure of the extent to which a learning machine has to keep revising its estimate of the number of mind changes it will make before converging to a correct hypothesis for languages in the class being learned. Recently, this notion, which also yields a measure for the difficulty of learning a class of languages, has been used to analyze the learnability of rich concept classes. The present paper further investigates the utility of ordinal mind change complexity. It is shown that for identification from both positive and negative data and n greater than or equal to 1, the ordinal mind change complexity of the class of languages formed by unions of up to n + 1 pattern languages is only omega x(O) notn(n) (where notn(n) is a notation for n, omega is a notation for the least limit ordinal and x(O) represents ordinal multiplication). This result nicely extends an observation of Lange and Zeugmann that pattern languages can be identified from both positive and negative data with 0 mind changes. Existence of an ordinal mind change bound for a class of learnable languages can be seen as an indication of its learning "tractability". Conditions are investigated under which a class has an ordinal mind change bound for identification from positive data. It is shown that an indexed family of languages has an ordinal mind change bound if it has finite elasticity and can be identified by a conservative machine. It is also shown that the requirement of conservative identification can be sacrificed for the purely topological requirement of M-finite thickness. Interaction between identification by monotonic strategies and existence of ordinal mind change bound is also investigated, (C) 1999 Elsevier Science B.V. All rights reserved.
ordinals mind change complexity inductive inference