'Average precision' presentation slideshows

Ranking with High-Order and Missing Information

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Information Retrieval Evaluation

Information Retrieval Evaluation. http://net.pku.edu.cn/~wbia ??? hle @net.pku.edu.cn ?????????? 1 0 / 29 /201 3. IR Evaluation Methodology. Measures for a search engine. ?? index ??? Number of documents/hour Documents size ????? ????? Latency as a function of index size

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Information Retrieval

Information Retrieval. January 14, 2005. Handout #2. Course Information. Instructor: Dragomir R. Radev (radev@si.umich.edu) Office: 3080, West Hall Connector Phone: (734) 615-5225 Office hours: M 11-12 & Th 12-1 or via email Course page: http://tangra.si.umich.edu/~radev/650/

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Information Retrieval (3)

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Bundling Features for Large Scale Partial-Duplicate Web Image Search

Bundling Features for Large Scale Partial-Duplicate Web Image Search. Zhong Wu ?, Qifa Ke , Michael Isard , and Jian Sun CVPR 2009 . Outline. Introduction Bundled features Image Retrieval using bundled feature Experiments and results Conclusion. Outline. Introduction

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Chapter 3 Retrieval Evaluation

Chapter 3 Retrieval Evaluation. Modern Information Retrieval Ricardo Baeza-Yates Berthier Ribeiro-Neto. Hsu Yi-Chen, NCU MIS 88423043. Outline. Introduction Retrieval Performance Evaluation Recall and precision Alternative measures Reference Collections TREC Collection

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Chapter 8

Chapter 8. Evaluating Search Engine. Evaluation. Evaluation is key to building effective and efficient search engines Measurement usually carried out in controlled laboratory experiments Online testing can also be done Effectiveness, efficiency, and cost are related

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Evaluation

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Content

Introduction to Information Retrieval (Manning, Raghavan, Schutze) Chapter 8 Evaluation and Result Summaries. Content. Results summaries: Making our good results usable to a user How do we know if our results are any good? Evaluating a search engine Benchmarks Precision and recall.

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A Statistical Analysis of the Precision-Recall Graph

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Object retrieval with large vocabularies and fast spatial matching

Object retrieval with large vocabularies and fast spatial matching. James Phibin 1 , Ondrej Chum 1 , Michael Isard 2 ,Josef Sivic 1 , and Andrew Zisserman 1 1 Department of Engineering Science, 2 University of Oxford Microsoft Research,Silicon Valley. CVPR 2007. Overview. Problem

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Tie-Breaking Bias: Effect of an Uncontrolled Parameter on Information Retrieval Evaluation

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HyKSS: A Multiple Ontology Approach to Hybrid Search

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Performance measures

Performance measures. Performance measures for matching. The following counts are typically measured for performance of matching : TP : true positives , i.e. number of correct matches FN : false negatives , matches that were not correctly detected

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Joint SemEval/CLEF tasks: Contribution of WSD to CLIR

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Mining Officially Unrecognized Side effects of drugs by combining Web Search and Machine learning

Mining Officially Unrecognized Side effects of drugs by combining Web Search and Machine learning. Carlo Carino, Yuanyuan Jia, Bruce Lambert, Patricia West and Clement Yu University of Illinois at Chicago. Motivation. Drugs have side effects

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Evaluation

Evaluation. INST 734 Module 5 Doug Oard. Agenda. Evaluation fundamentals Test collections: evaluating sets Test collections: evaluating rankings Interleaving User studies. = relevant document. Which is the Best Rank Order?. A. B. C. D. E. F. = relevant document.

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Exploring the Similarity Space

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Reiner Kraft rekraft@cse.ucsc

A machine learning approach to improve precision for navigational queries in a Web information retrieval system. Reiner Kraft rekraft@cse.ucsc.edu. Motivation. Ranking of search results: Require high precision vs. recall

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Visual Phrases

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