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Skip to Search Results- 85Artificial Intelligence
- 34Machine Learning
- 30Natural Language Processing
- 18Bioinformatics
- 13Reinforcement Learning
- 8Deep Learning
- 4Müller, Martin
- 4Szafron, Duane
- 3Lu, Paul
- 3Mueller, Martin
- 3Pelletier, Francis J.
- 3Schaeffer, Jonathan
- 81Graduate and Postdoctoral Studies (GPS), Faculty of
- 81Graduate and Postdoctoral Studies (GPS), Faculty of/Theses and Dissertations
- 23Computing Science, Department of
- 23Computing Science, Department of/Technical Reports (Computing Science)
- 5WISEST Summer Research Program
- 5WISEST Summer Research Program/WISEST Research Posters
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1982
Pelletier, Francis J., Schubert, Lenhart K.
Introduction: We describe an approach to parsing and logical translation that was inspired by Gazdar's work on context-free grammar for English. Each grammar rule consists of a syntactic part that specifies an acceptable fragment of a parse tree, and a semantic part that specifies how the logical...
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1991
Pelletier, Francis J., Schubert, Lenhart
Introduction: This very short book is apparently intended as a supplementary text in a graduate AI course. The author describes it as a \"text and reference work on the applications of non-standard logics to artificial intelligence (AI).\" It gives short and concise (too short and too concise, in...
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1992
Technical report TR92-19. In August 1992, the first man versus machine world championship took place. The champion, Dr. Marion Tinsley, is arguably the greatest checkers player that ever lived. The challenger was the computer checkers program Chinook, a 3 year team effort from the University of...
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[Review of the book Formal Methods in Artificial Intelligence, by Aamsay]
1996
Introduction: Many universities teach artificial intelligence (AI) by having one undergraduate course that introduces students to a very wide variety of topics, usually including search and search heuristics, representational systems (including formal logic), problem solving, vision, expert...
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2001
Schaeffer, Jonathan, Charter, K., Lu, Paul, Szafron, Duane, Parsons, I., Driga, A.
Technical report TR01-10. For two DNA or protein sequences of length m and n, dynamic programming alignment algorithms like Needleman-Wunsch and Smith-Waterman take O(m x n) time and use O(m x n) space, so we refer to them as full matrix (FM) algorithms. This space requirement means that large...
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2002
Fortin, David, Antoniu, Angela, Sardarli, Arzu, Rezania, Vahid, Levner, Ilya, Bulitko, Vadim
Technical report TR02-14. The 2002 Quantum Computing Summer School (QCSS'02) at the University of Alberta was organized as a learning and discussion forum for researchers in Artificial Intelligence, Computer Science, Physics, Mathematics, and Engineering. The short-term objective was to introduce...
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Identification of a novel lipase gene mutated in lpd mice with hypertriglyceridemia and associated with dyslipidemia in humans
Download2003-05-13
Wen, Xiao-Yan, Hegele, Robert A., Wang, Jian, Wang, Ding Yan, Cheung, Joseph, Wilson, Michael, Yahyapour, Maryam, Bai, Yahong, Zhuang, Lihua, Skaug, Jennifer, Young, T. Kue, Connelly, Philip W., Koop, Ben F., Tsui, Lap-Chee, Stewart, A. Keith
"Triglyceride (TG) metabolism is crucial for whole body and local energy homeostasis and accumulating evidence suggests an independent association between plasma TG concentration and increased atherosclerosis risk. We previously generated a mouse insertional mutation lpd (lipid defect) whose...
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2003
Greiner, Russell, Wishart, David, Eisner, Roman, Lu, Z., Lu, Paul, Macdonell, Cam, Poulin, B., Szafron, Duane, Anvik, J.
Technical report TR03-14. Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy...
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2003
Greiner, Russ, Poulin, B., Lu, Paul, Anvik, J., Lu, Z., Macdonell, Cam, Wishart, David, Eisner, Roman, Szafron, Duane
Technical report TR03-09. Naive Bayes classifiers, a popular tool for predicting the labels of query instances, are typically learned from a training set. However, since many training sets contain noisy data, a classifier user may be reluctant to blindly trust a predicted label. We present a...