Dissertations, Theses, and Capstone Projects
Date of Degree
2010
Document Type
Dissertation
Degree Name
Ph.D.
Program
Psychology
Advisor
Robert Melara
Committee Members
Vivien C. Tartter
James B. Marshall
Martin Chodorow
Heng Ji
Subject Categories
Developmental Psychology | Psychology
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.
Recommended Citation
Robare, Rebecca, "Semi-Supervised Learning for Connectionist Networks" (2010). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/1935
Comments
Digital reproduction from the UMI microform.