Knowledge-based Cascade-correlation is a neural network algorithm that combines inductive learning and knowledge transfer (Shultz & Rivest, 2001). In the present thesis, this algorithm is tested on several real-world and artificial problems, and extended in several ways. The first extension consists in the incorporation of the Knowledge-based Artificial Neural Network (KBANN; Shavlik, 1994) technique for generating rule-based (RBCC) networks. The second extension consists of the adaptation of the Optimal Brain Damage (OBD; LeCun, Denker, & Solla, 1990) pruning technique to remove superfluous connection weights. Finally, the third extension consists in a new objective function based on information theory for controlling the distribution of knowledge attributed to subnetworks. A simulation of lexical ambiguity resolution is proposed. In this study, the use of RBCC networks is motivated from a cognitive and neurophysiological perspective.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.111824 |
| Date | January 2005 |
| Creators | Thivierge, Jean-Philippe. |
| Publisher | McGill University |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | application/pdf |
| Coverage | Doctor of Philosophy (Department of Psychology.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 002511605, proquestno: AAINR25267, Theses scanned by UMI/ProQuest. |
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