Types of monotonic language learning and their characterization
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
On the role of procrastination in machine learning
Information and Computation
Language learning from texts: mindchanges, limited memory, and monotonicity
Information and Computation
Incremental learning from positive data
Journal of Computer and System Sciences
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Ordinal mind change complexity of language identification
Theoretical Computer Science
Incremental concept learning for bounded data mining
Information and Computation
The power of procrastination in inductive inference: How it depends on used ordinal notations
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Feasible Iteration of Feasible Learning Functionals
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Learning with Temporary Memory
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Incremental learning with ordinal bounded example memory
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
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A bounded example memory learner operates incrementally and maintains a memory of finitely many data items. The paradigm is well-studied and known to coincide with set-driven learning. A hierarchy of stronger and stronger learning criteria had earlier been obtained when one considers, for each k@?N, iterative learners that can maintain a memory of at most k previously processed data items. We investigate an extension of the paradigm into the constructive transfinite. For this purpose we use Kleene@?s universal ordinal notation system O. To each ordinal notation in O one can associate a learning criterion in which the number of times a learner can extend its example memory is bounded by an algorithmic count-down from the notation. We prove a general hierarchy result: if b is larger than a in Kleene@?s system, then learners that extend their example memory ''at most b times'' can learn strictly more than learners that can extend their example memory ''at most a times''. For notations for ordinals below @w^2 the result only depends on the ordinals and is notation-independent. For higher ordinals it is notation-dependent. In the setting of learners with ordinal-bounded memory, we also study the impact of requiring that a learner cannot discard an element from memory without replacing it with a new one. A learner satisfying this condition is called cumulative.