PPT-Probabilistic Information Retrieval

Chris Manning Pandu Nayak and Prabhakar Raghavan Who are these people Stephen Robertson Keith van Rijsbergen Karen Sp ä rck Jones Summary vector space ranking Represent the query as a weighted tfidf vector. (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. Pattern Completion and Recapitulation. Episodic Retrieval and the Frontal Lobes. Cues for Retrieval. The Second Time Around: Recognizing Stimuli by Recollection and Familiarity. Misremembering the Past. Hui Fang , Tao . Tao. , . ChengXiang. . Zhai. University of Illinois at Urbana Champaign. SIGIR 2004 Best Paper. Presented by Lingjie Zhang. Outline. Formal Definitions of Heuristic Retrieval Constraints. INST 734. Module 3. Doug . Oard. Agenda. Ranked retrieval. Similarity-based ranking. Probability-based ranking. Boolean Retrieval. Strong points. Accurate, . if you know the right strategies. Efficient for the computer. INST 734. Doug . Oard. Module 13. Agenda. Image retrieval. Video retrieval. Multimedia retrieval. Multimedia. A set of time-synchronized modalities. Video. Images, object motion, camera motion, scenes. Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. . Models. . 1. Overview. . Probabilistic Approach to Retrieval. . Basic Probability Theory. Binary Independence . Model. Bayesian Model. 2. Outline. . Probabilistic Approach to Retrieval. . Basic Probability Theory. (for MODIS). Andy Harris. Jonathan . Mittaz. Prabhat. . Koner. (Chris Merchant, Pierre . LeBorgne. ). Satellite data – pros and cons. Main advantages of satellite data. Frequent and regular global coverage (cloud cover permitting for IR). Hongning. Wang. CS@UVa. What is information retrieval?. CS6501: Information Retrieval. CS@UVa. 2. Why information retrieval . Information overload. “. It refers to the . difficulty. a person can have understanding an issue and making decisions that can be caused by the presence of . All slides ©Addison Wesley, 2008. How Much Data is Created Every . Minute?. Source: . https. ://www.domo.com/blog/2012/06/how-much-data-is-created-every-minute/. The Search Problem. Search and Information Retrieval. What is IR?. Sit down before fact as a little child, . be prepared to give up every conceived notion, . follow humbly wherever and whatever abysses nature leads, . or you will learn nothing. . . -- Thomas Huxley --. Chapter 5: Probabilistic Query Answering (3). 2. Objectives. In this chapter, you will:. Learn the definition and query processing techniques of a probabilistic query type. Probabilistic Reverse Nearest Neighbor Query. Chapter 7: Probabilistic Query Answering (5). 2. Objectives. In this chapter, you will:. Explore the definitions of more probabilistic query types. Probabilistic skyline query. Probabilistic reverse skyline query. Nathan Clement. Computational Sciences Laboratory. Brigham Young University. Provo, Utah, USA. Next-Generation Sequencing. Problem Statement . Map next-generation sequence reads with variable nucleotide confidence to .