On the role of procrastination in machine learning
Information and Computation
Elementary formal systems, intrinsic complexity, and procrastination
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Generalized notions of mind change complexity
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
General Inductive Inference Types Based on Linearly-Ordered Sets
STACS '96 Proceedings of the 13th Annual Symposium on Theoretical Aspects of Computer Science
Not-So-Nearly-Minimal-Size Program Inference
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
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
Derived Sets and Inductive Inference
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
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In this paper, we reconsider the definition of procrastinating learning machines. In the original definition of Freivalds and Smith [FS93], constructive ordinals are used to bound mindchanges. We investigate possibility of using arbitrary linearly ordered sets to bound mindchanges in similar way. It turns out that using certain ordered sets it is possible to define inductive inference types different from the previously known ones. We investigate properties of the new inductive inference types and compare them to other types.