521 |
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).
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522 |
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.
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523 |
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).
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524 |
Generic mechanistic modeling for multi-axis machining /Bailey, Trevor Eric. January 2001 (has links)
Thesis (Ph.D.) -- McMaster University, 2001. / Includes bibliographical references (leaves 251-260). Also available via World Wide Web.
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525 |
Haptic system for collision-free 5-axis tool path generation /Chen, Kun. January 2006 (has links)
Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 83-89). Also available in electronic version.
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526 |
Matrix nearness problems in data miningSra, Suvrit, January 1900 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.
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527 |
Robot developmental learning of an object ontology grounded in sensorimotor experienceModayil, Joseph Varughese. January 1900 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.
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528 |
Bit-sliced design of a graph reduction processor /Vireday, Richard Pierre, January 1986 (has links)
Thesis (M.S.)--Oregon Graduate Center, 1986.
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529 |
Activity Recognition from Physiological Data using Conditional Random FieldsChieu, 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)
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530 |
Data mining logic explanations from numerical data /Riehl, Katrina. January 2006 (has links)
Thesis. / Includes vita. Includes bibliographical references (leaves 79-86)
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