Semi-supervised learning for connectionist networks

Title: Semi-supervised learning for connectionist networks
Identifier: d_2009_2013:507b67c397f8:10665
identifier: 10834
Creator: Robare, Rebecca J.,
Contributor: Robert D. Melara
Date: 2010
Language: English
Publisher: City University of New York.
Subject: Cognitive psychology | Computer science | Developmental psychology | connectionism | language acquisition | lexical acquisition | neural networks | semi-supervised learning
Abstract: 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.
Type: dissertation
Source: 2009_2013.csv
degree: Ph.D.
Program: Psychology