Abstract/Details

Multi-layer in-place learning for autonomous mental development


2006 2006

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Abstract (summary)

Cortical self-organization during open-ended development is a core issue for perceptual development. Traditionally, unsupervised learning and supervised learning are two different types of learning conducted by different networks. However, there is no evidence that the biological nervous system treats them in a disintegrated way. The computational model presented here integrates both types of learning using a new biologically inspired network whose learning is in place. By in-place learning, we mean that each neuron in the network learns on its own while interacting with other neurons. There is no need for a separate learning network. Presented in this thesis is the Multi-layer In-place Learning Network (MILN) for regression and classification. This work concentrates on its two-layer version (without incorporating an attention selection mechanism). The network enables both unsupervised and supervised learning to occur concurrently. Within each layer, the adaptation of each neuron is nearly-optimal in the sense of the least possible estimation error given the observations. MILN is intended to provide memory organization capability and to be used as a general-purpose regressor in Autonomous Mental Development. The theory behind the network is discussed in detail. Experimental results are presented to show the effects of the properties investigated.

Indexing (details)


Subject
Computer science
Classification
0984: Computer science
Identifier / keyword
Applied sciences
Title
Multi-layer in-place learning for autonomous mental development
Author
Luciw, Matthew D.
Number of pages
108
Publication year
2006
Degree date
2006
School code
0128
Source
MAI 45/01M, Masters Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780542899119
Advisor
Weng, Juyang
University/institution
Michigan State University
University location
United States -- Michigan
Degree
M.S.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
1438132
ProQuest document ID
305313045
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305313045
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