FINite learning is a model of learning from examples where the learning algorithm is permitted to conjecture only one hypothesis, i.e., cannot change its mind. The types of things being learned areâ€¦ (More)

In this paper we show that it is always possibk to reduce errors for some forms of inductive Inference by increasing the numller of machines involved in the inference process. Moreover, we obtainâ€¦ (More)

We show that for every probabilistic FIN-type learner with success ratio greater than 24/49, there is another probabilistic FIN-type learner with success ratio 1/2 that simulates the former. We willâ€¦ (More)

Introduction. Throughout the history of the theory of recursive functions diverse hierarchies have been proposed in order to study and classify both constructive and nonconstructive objects.â€¦ (More)

We consider the power of randomization in finite learning when a bounded number of mind changes are allowed. We show that in the ~m+2_3 range ( ~~+a _2, 1] the capability type of probabilistic asâ€¦ (More)

Busy beaver sets have proved themselves useful as examples for certain interesting properties in recursive function theory and computational complexity which are not easily demonstrated and as aâ€¦ (More)