PPT-Meta Structure: Computing Relevance in Large Heterogeneous Information Networks

Author : test | Published Date : 2018-03-07

Outline 1 Introduction 2 Meta Structure 3 Relevance Measures 4 Experiments Introduction Computing relevance on network social network coauthor

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Meta Structure: Computing Relevance in Large Heterogeneous Information Networks: Transcript

Outline 1 Introduction 2 Meta Structure 3 Relevance Measures 4 Experiments Introduction Computing relevance on network social network coauthor. Paul N. Bennett, Microsoft Research. Joint with. Ece Kamar, Microsoft Research. Gabriella Kazai, Microsoft Research Cambridge. Motivation for Consensus Task. Recover actual . relevance of . a topic-document . By . Rong. Yan, Alexander G. and . Rong. Jin. Mwangi. S. . Kariuki. 2008-11629. Quiz. What’s Negative Pseudo-Relevance feedback in multimedia retrieval?. Introduction. As a result of high demand of content based access to video information.. 8. LEARNING OBJECTIVES. Identify advantages and disadvantages of each of the four main types of wireless transmission media.. Explain how businesses can use technology employed by . short-range. , medium-range, and . vs.. Weak Induction. Homework. Study Fallacies 1-18. Review pp. 103-132. Fallacies (definition § 4.1). § 4.2 Fallacies of Relevance (1 – 8). § 4.3 Fallacies of Weak Induction (9 – 14). For Next Class: pp. 139-152. Jonathan Kuck. 1. , . Honglei. Zhuang. 1. , . Xifeng. Yan. 2. , Hasan Cam. 3. , . Jiawei. Han. 1. 1. University of Illinois at Urbana-Champaign. 2. University of California at Santa Barbara. 3. US Army Research Lab. Hang Xiao. Background. Feature. a . feature. is an individual . measurable heuristic property of a phenomenon being observed. In character recognition: . horizontal and vertical . profiles, . number of internal holes, stroke . David Collings (ECU) and Bruce Guthrie (GCA. ). In this session: . Supplementing the UES. Why workplace relevance?. WRS Development. Source, versions, items. Workplace Relevance Scale. Dennis . Trewen. J. Max Wawrik. Nancy Rosado Colon. Law 16. Spring 2017. Law of Evidence. . . Key Terms. Adversary System (U.S.). A system of justice where the parties work in opposition to each . other, and each party tries to win a favorable result for itself. The . C. ontrol of . H. eterogeneous . L. arge-Scale Systems of . A. utonomous . V. ehicles (. TECHLAV. ). TECHLAV Annual Meeting. Greensboro, NC. May 31-June 1, 2017. http://techlav.ncat.edu. /. Task Allocation Using Parallelized Clustering and Auctioning Algorithms for Heterogeneous Robotic Swarms Operating on a Cloud Network. Clickthroughs. for News Search. Hongning. Wang. . , . Anlei. Dong. *. , . Lihong. Li. *. , Yi Chang. *. , . Evgeniy. . Gabrilovich. *. . CS@UIUC . *. Yahoo! Labs. Relevance . v.s. . Freshness. Objective: develop technologies to improve computer performance. . . 1. Processor. Generation. Max. Clock. Speed (GHz). Max. Numberof Cores. Max. RAM. Bandwidth (GB/s). Max. Peak Floating Point (Gflop/s). Dept. of Computer Science and Engineering. University of South Carolina. Dr. Jason D. . Bakos. Assistant Professor. Heterogeneous and Reconfigurable Computing Lab (HeRC). This material is based upon work supported by the National Science Foundation under Grant Nos. CCF-0844951 and CCF-0915608.. Information . Networks. Yangqiu. . Song. Department of CSE, HKUST, Hong Kong. 1. Joint work with many collaborators; Slides Credit: . Chenguang. Wang, . Huan. Zhao. , . Yanfang. (Fanny) Ye. Homo. 1. 2. Take-. away. . today. Interactive relevance feedback:. improve initial retrieval results by telling the IR system which docs are relevant / . nonrelevant. Best known relevance feedback method: .

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