University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Integrated Learning in Multi-net Systems

Casey, MC (2004) Integrated Learning in Multi-net Systems Doctoral thesis, University of Surrey.

2004 Casey Integrated Learning in Multi-net Systems.pdf
Available under License : See the attached licence file.

Download (1MB)
[img] Text (licence)
Available under License Creative Commons Attribution Non-commercial Share Alike.

Download (1kB)


Specific types of multi-net neural computing systems can give improved generalisation performance over single network solutions. In single-net systems learning is one way in which good generalisation can be achieved, where a number of neurons are combined through a process of collaboration. In this thesis we examine collaboration in multi-net systems through in-situ learning. Here we explore how generalisation can be improved through learning in the components and their combination at the same time. To achieve this we present a formal way in which multi-net systems can be described in an attempt to provide a method with which the general properties of multi-net systems can be explored. We then explore two novel learning algorithms for multi-net systems that exploit in-situ learning, evaluating them in comparison with multi-net and single-net solutions. Last, we simulate two cognitive processes with in-situ learning to examine the interaction between different numerical abilities in multi-net systems. Using single-net simulations of subitization and counting we build a multi-net simulation of quantification. Similarly, we combine single-net simulations of the fact retrieval and ‘count all’ addition strategies into a multi-net simulation of addition. Our results are encouraging, with improved generalisation performance obtained on benchmark problems, and the interaction of strategies with in-situ learning used to describe well known numerical ability phenomena. This learning through interaction in connectionist simulations we call integrated learning.

Item Type: Thesis (Doctoral)
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors : Casey, MC
Date : 1 February 2004
Uncontrolled Keywords : Neural Networks, Multi-net Systems, Numerical Abilities
Depositing User : Symplectic Elements
Date Deposited : 17 Jun 2011 13:56
Last Modified : 06 Jul 2019 05:08

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800