Sparse distributed memory: Difference between revisions

Revision as of 03:41, 27 December 2023

Revision as of 03:49, 29 December 2023

Line 240:

===Reinforcement learning===

SDMs provide a linear, local [[function approximation]] scheme, designed to work when a very large/high-dimensional input (address) space has to be mapped into a much smaller [[Computer memory|physical memory]]. In general, local architectures, SDMs included, can be subject to the [[curse of dimensionality]], as some target functions may require, in the worst case, an exponential number of local units to be approximated accurately across the entire input space. However, it is widely believed that most [[Decision support system|decision-making systems]] need high accuracy only around low-dimensional [[manifolds]] of the [[state space]], or important state "highways".<ref>Ratitch, Bohdana, Swaminathan Mahadevan, and [[Doina Precup]]. "Sparse distributed memories in reinforcement learning: Case studies." Proc. of the Workshop on Learning and Planning in Markov Processes-Advances and Challenges. 2004.</ref> The work in Ratitch et al.<ref>Ratitch, Bohdana, and Doina Precup. "[http://www.cs.mcgill.ca/~dprecup/temp/ecml2004.pdf Sparse distributed memories for on-line value-based reinforcement learning] {{Webarchive|url=https://web.archive.org/web/20150824061329/http://www.cs.mcgill.ca/~dprecup/temp/ecml2004.pdf |date=2015-08-24 }}." Machine Learning: ECML 2004. Springer Berlin Heidelberg, 2004. 347-358.</ref> combined the SDM memory model with the ideas from [[instance-based learning|memory-based learning]], which provides an approximator that can dynamically adapt its structure and resolution in order to locate regions of the state space that are "more interesting"<ref>Bouchard-Côté, Alexandre. "[https://www.stat.ubc.ca/~bouchard/pub/report-ml.pdf Sparse Memory Structures Detection]." (2004).</ref> and allocate proportionally more memory resources to model them accurately.

===Object indexing in computer vision===