Dissertation/Thesis Abstract

Semi-supervised learning for connectionist networks
by Robare, Rebecca J., Ph.D., City University of New York, 2010, 117; 3426834
Abstract (Summary)

At the computational level, language is often assumed to require both supervised and unsupervised learning. Although we have a certain understanding of these computational processes both biologically and behaviorally, our understanding of the environmental conditions under which language learning takes place falls short. I examine the semi-supervised learning paradigm as the most accurate computational description of the environmental conditions of lexical acquisition during language development. This paradigm is assessed for task learning and generalization and I argue that its real ecological validity and occasional improvements in performance over supervised learning make it an ideal candidate for modeling of language acquisition and other learning problems.

Indexing (document details)
Advisor: Melara, Robert D.
Commitee: Chodorow, Martin, Ji, Heng, Marshall, James B., Tartter, Vivien C.
School: City University of New York
Department: Psychology
School Location: United States -- New York
Source: DAI-B 71/11, Dissertation Abstracts International
Subjects: Developmental psychology, Cognitive psychology, Computer science
Keywords: Connectionism, Language acquisition, Lexical acquisition, Neural networks, Semi-supervised learning
Publication Number: 3426834
ISBN: 978-1-124-29092-8
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