This thesis studies the introduction of a priori structure into the design of learning systems based on artificial neural networks applied to sequence recognition, in particular to phoneme recognition in continuous speech. Because we are interested in sequence analysis, algorithms for training recurrent networks are studied and an original algorithm for constrained recurrent networks is proposed and test results are reported. We also discuss the integration of connectionist models with other analysis tools that have been shown to be useful for sequences, such as dynamic programming and hidden Markov models. We introduce an original algorithm to perform global optimization of a neural network/hidden Markov model hybrid, and show how to perform such a global optimization on all the parameters of the system. Finally, we consider some alternatives to sigmoid networks: Radial Basis Functions, and a method for searching for better learning rules using a priori knowledge and optimization algorithms.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.70220 |
| Date | January 1991 |
| Creators | Bengio, Yoshua |
| 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 (School of Computer Science.) |
| Rights | All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated. |
| Relation | alephsysno: 001255001, proquestno: AAINN72116, Theses scanned by UMI/ProQuest. |
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