Search results for "Inductive bias"
showing 4 items of 4 documents
Incorporating hypothetical knowledge into the process of inductive synthesis
1996
The problem of inductive inference of functions from hypothetical knowledge is investigated in this paper. This type of inductive inference could be regarded as a generalization of synthesis from examples that can be directed not only by input/output examples but also by knowledge of, e. g., functional description's syntactic structure or assumptions about the process of function evaluation. We show that synthesis of this kind is possible by efficiently enumerating the hypothesis space and illustrate it with several examples.
Learning from good examples
1995
The usual information in inductive inference for the purposes of learning an unknown recursive function f is the set of all input /output examples (n,f(n)), n ∈ ℕ. In contrast to this approach we show that it is considerably more powerful to work with finite sets of “good” examples even when these good examples are required to be effectively computable. The influence of the underlying numberings, with respect to which the learning problem has to be solved, to the capabilities of inference from good examples is also investigated. It turns out that nonstandard numberings can be much more powerful than Godel numberings.
Memory limited inductive inference machines
1992
The traditional model of learning in the limit is restricted so as to allow the learning machines only a fixed, finite amount of memory to store input and other data. A class of recursive functions is presented that cannot be learned deterministically by any such machine, but can be learned by a memory limited probabilistic leaning machine with probability 1.
Inductive inference of recursive functions: Qualitative theory
2005
This survey contains both old and very recent results in non-quantitative aspects of inductive inference of total recursive functions. The survey is not complete. The paper was written to stress some of the main results in selected directions of research performed at the University of Latvia rather than to exhaust all of the obtained results. We concentrated on the more explored areas such as the inference of indices in non-Goedel computable numberings, the inference of minimal Goedel numbers, and the specifics of inference of minimal indices in Kolmogorov numberings.