PPT-IR Theory: Relevance Feedback

Relevance Feedback Example Initial Results Search Engine 2 Relevance Feedback Example Relevance Feedback Search Engine 3 Relevance Feedback Example Revised Results Search Engine 4. for 2015 Baldrige Award Applicants . “Straight A” Feedback Comments. A. ctionable. . A. ccurate . A. dequate. A. ligned*. *. Added Emphasis in 2015. Actionable:. . relevant to the applicant’s key factors and specific enough that the organization can use the feedback to sustain or improve its . Multimedia Databases. via Relevance Feedback . with History and Foresight Support. DBRank. 08, April 12. th. 2008, . Cancún. , Mexico. Marc Wichterich. , Christian Beecks, Thomas Seidl. Outline. Motivation. Earth Mover‘s Distance. Marc Wichterich. , Christian Beecks, Martin Sundermeyer, Thomas Seidl. Data Management and Data Exploration Group. RWTH Aachen University, Germany. Introduction. Distance-based Adaptable Similarity Search. 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.. 2011-11709. Seo. . Seok. . Jun. Abstract. Video information retrieval. Finding info. relevant to query. Approach. Pseudo-relevance feedback. Negative PRF. Questions. How this paper approach to content-based video retrieval. Relevance Feedback . Relevance Feedback: . Example. Initial Results. Search Engine. 2. Relevance Feedback: . Example. Relevance Feedback. Search Engine. 3. Relevance Feedback: . Example. Revised Results. Young Scientist’s 1st International Baku Forum. The Relevance of Media Coverage. Bashar Ibrahim . AlHadla. The central concept of media audience is the relationship between the sender and the receiver.. . Schütze. and Christina . Lioma. Lecture 9: Relevance Feedback & Query Expansion. 1. 2. Take-. away. . today. Interactive relevance feedback:. improve initial retrieval results by telling the IR system which docs are relevant / . Earth Mover‘s Distance. Marc Wichterich. , Christian Beecks, Martin Sundermeyer, Thomas Seidl. Data Management and Data Exploration Group. RWTH Aachen University, Germany. Introduction. Distance-based Adaptable Similarity Search. PRSA – Detroit Chapter . APR . Accreditation Prep. Lolita . Cummings Carson, APR. Media, Public & Policy Makers (each has an agenda). Agenda – a set of issues that are communicated. Multi-directional (any party can begin the process). for Pseudo–Relevance Feedback . Yuanhua . Lv. . & . ChengXiang. . Zhai. Department of Computer Science, UIUC. Presented by Bo Man . 2014/11/18. Positional Relevance Model . for Pseudo–Relevance Feedback . Theory Feedback. Theory / SLA. Generally, students view Theory as one of the most difficult courses…. There is a lot of reading. (at the beginning). There is a lot of writing. But….. Students tend to like . Inspirations, ideas . &. plans. Motivation. Ideal situation: general-purpose image annotation with unlimited vocabulary. Reality:. Classifiers with limited vocabulary and dependency on labeled training data. 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: .