article · eScholarship (California Digital Library) · 2004
Knowledge, experience, generations, and limits in machine learning
Burgin, Mark, Klinger, Allen
Cited by 0Open access
Abstract
This paper extends traditional models of machine learning beyond their one-level structure by introducing previously obtained problem knowledge into the algorithm or automaton involved. Some authors studied more advanced than traditional models that utilize some kind of predetermined knowledge, having a two-level structure. However, even in this case, the model has not reflected the source and inherited properties of predetermined knowledge. In society, knowledge is often transmitted from previous generations. The aim of this paper is to construct and study algorithmic models of learning processes that utilize predetermined or prior knowledge. The models use recursive, subrecursive, and super-recursive algorithms. Predetermined knowledge includes: a text description, activity rules (e.g., for cognition), and specific structured personal or social memory. Algorithmic models represent these three forms as separate structured processing systems: automata with (1) advice; (2) structured program; and (3) structured memory. That yields three basic models for learning systems: polynomially bounded turing machines, Turing machines, and inductive Turing machines of the first order. (C) 2003 Elsevier B.V. All rights reserved.
Cite this paper
Burgin, M., & Klinger, A. (2004). Knowledge, experience, generations, and limits in machine learning. eScholarship (California Digital Library).
Burgin, Mark, and Allen Klinger. “Knowledge, experience, generations, and limits in machine learning.” eScholarship (California Digital Library), 2004.
Burgin, Mark, and Allen Klinger. 2004. “Knowledge, experience, generations, and limits in machine learning.” eScholarship (California Digital Library).
Burgin, M. and Klinger, A. (2004) ‘Knowledge, experience, generations, and limits in machine learning’, eScholarship (California Digital Library).
@article{burgin2004knowledge,
title = {{Knowledge, experience, generations, and limits in machine learning}},
author = {Burgin, Mark and Klinger, Allen},
journal = {eScholarship (California Digital Library)},
year = {2004}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.