Global ETD Search

201	Performance characterization of boosting in computer vision / Li, Weiliang. January 2005 (has links) Thesis (Ph. D.)--Lehigh University, 2005. / Includes vita. Includes bibliographical references (leaves 163-177).
202	Protein secondary structure prediction using conditional random fields and profiles / Shen, Rongkun. January 1900 (has links) Thesis (M.S.)--Oregon State University, 2006. / Printout. Includes bibliographical references (leaves 42-46). Also available on the World Wide Web.
203	Semi-supervised distance metric learning / Chang, Hong. January 2006 (has links) Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 120-137). Also available in electronic version.
204	Learning real-world problems by finding correlated basis functions / Drake, Adam C., January 2006 (has links) (PDF) Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 79-81).
205	Broad-coverage hierarchical word sense disambiguation / Ciaramita, Massimiliano. January 2005 (has links) Thesis (Ph.D.)--Brown University, 2005. / Vita. Thesis advisor: Mark Johnson. Includes bibliographical references (leaves 127-138). Also available online.
206	Learning successful strategies in repeated general-sum games / Crandall, Jacob W., January 2005 (has links) (PDF) Thesis (Ph.D.)--Brigham Young University. Dept. of Computer Science, 2005. / Includes bibliographical references (p. 163-168).
207	Matrix nearness problems in data mining Sra, Suvrit, January 1900 (has links) Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references. Data mining. Machine learning. Matrices.
208	Robot developmental learning of an object ontology grounded in sensorimotor experience Modayil, Joseph Varughese. January 1900 (has links) Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.
209	Activity Recognition from Physiological Data using Conditional Random Fields Chieu, Hai Leong, Lee, Wee Sun, Kaelbling, Leslie P. 01 1900 (has links) We describe the application of conditional random fields (CRF) to physiological data modeling for the application of activity recognition. We use the data provided by the Physiological Data Modeling Contest (PDMC), a Workshop at ICML 2004. Data used in PDMC are sequential in nature: they consist of physiological sessions, and each session consists of minute-by-minute sensor readings. We show that linear chain CRF can effectively make use of the sequential information in the data, and, with Expectation Maximization, can be trained on partially unlabeled sessions to improve performance. We also formulate a mixture CRF to make use of the identities of the human subjects to further improve performance. We propose that mixture CRF can be used for transfer learning, where models can be trained on data from different domains. During testing, if the domain of the test data is known, it can be used to instantiate the mixture node, and when it is unknown (or when it is a completely new domain), the marginal probabilities of the labels over all training domains can still be used effectively for prediction. / Singapore-MIT Alliance (SMA) Machine Learning Graphical Models Applications
210	Data mining logic explanations from numerical data / Riehl, Katrina. January 2006 (has links) Thesis. / Includes vita. Includes bibliographical references (leaves 79-86) Data mining. Machine learning. Logic.

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