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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
11

Evaluating the engaged institution the conceptualizations and discourses of engagement /

Steel, Victoria A. Placier, Peggy. January 2009 (has links)
Title from PDF of title page (University of Missouri--Columbia, viewed on March 1, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Peggy Placier. Vita. Includes bibliographical references.
12

An analysis of right-and left-brain thinkers and certain styles of learning

Bielefeldt, Steven D. January 2006 (has links) (PDF)
Thesis, PlanB (M.S.)--University of Wisconsin--Stout, 2006. / Includes bibliographical references.
13

Graph based semi-supervised learning in computer vision

Huang, Ning, January 2009 (has links)
Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Biomedical Engineering." Includes bibliographical references (p. 54-55).
14

Kernel methods in supervised and unsupervised learning /

Tsang, Wai-Hung. January 2003 (has links)
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 46-49). Also available in electronic version. Access restricted to campus users.
15

Imprisoned intelligence the discovery of undiagnosed learning disabilities in adults /

Orenstein, Myrna. January 1992 (has links) (PDF)
Dissertation (Ph.D.) -- The Institute for Clinical Social Work, 1992. / A dissertation submitted to the faculty of the Institute of Clinical Social Work in partial fulfillment for the degree of Doctor of Philosophy.
16

The effects of testing accommodations on the admissions test scores of students with learning disabilities /

Zurcher, Raymond John, January 1999 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 1999. / Vita. Includes bibliographical references (leaves 134-146). Available also in a digital version from Dissertation Abstracts.
17

Complex instruction giving students the education they deserve /

Tobias, Cindel K. January 2010 (has links) (PDF)
Thesis (M.I.T.)--The Evergreen State College, 2010. / Title from title screen (viewed 7/7/2010). Includes bibliographical references (leaves 161-167).
18

Product innovation learning in a small firm : a case study approach

Yeoh, Chiar Chuen January 1995 (has links)
No description available.
19

Towards autonomy and 'responsibility for learning' in organisations

Oudtshoorn, Mike van January 1992 (has links)
No description available.
20

Shrunken learning rates do not improve AdaBoost on benchmark datasets

Forrest, Daniel L. K. 30 November 2001 (has links)
Recent work has shown that AdaBoost can be viewed as an algorithm that maximizes the margin on the training data via functional gradient descent. Under this interpretation, the weight computed by AdaBoost, for each hypothesis generated, can be viewed as a step size parameter in a gradient descent search. Friedman has suggested that shrinking these step sizes could produce improved generalization and reduce overfitting. In a series of experiments, he showed that very small step sizes did indeed reduce overfitting and improve generalization for three variants of Gradient_Boost, his generic functional gradient descent algorithm. For this report, we tested whether reduced learning rates can also improve generalization in AdaBoost. We tested AdaBoost (applied to C4.5 decision trees) with reduced learning rates on 28 benchmark datasets. The results show that reduced learning rates provide no statistically significant improvement on these datasets. We conclude that reduced learning rates cannot be recommended for use with boosted decision trees on datasets similar to these benchmark datasets. / Graduation date: 2002

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