'Concept space' presentation slideshows

Analysis Environments For Functional Genomics

Analysis Environments For Functional Genomics. Bruce R. Schatz Institute for Genomic Biology University of Illinois at Urbana-Champaign schatz@uiuc.edu , www.beespace.uiuc.edu. Informatics Research First Annual BeeSpace Workshop June 6, 2005. What are Analysis Environments.

506 views • 41 slides

CS 430 / INFO 430 Information Retrieval

CS 430 / INFO 430 Information Retrieval. Lecture 26 Thesauruses and Cluster Analysis 2. Course Administration. CS 490 and CS 790 Independent Research Projects

412 views • 41 slides

THE NET OF THE 21st CENTURY: Concepts across the Interspace

THE NET OF THE 21st CENTURY: Concepts across the Interspace. Bruce Schatz CANIS Laboratory Graduate School of Library and Information Science University of Illinois at Urbana-Champaign www.canis.uiuc.edu, schatz@uiuc.edu. CNI Spring Meeting April 26, 1999 Washington, DC.

409 views • 40 slides

Selection of Relevant Features and Examples in Machine Learning

Selection of Relevant Features and Examples in Machine Learning. Paper By: Avrim L. Blum Pat Langley. Presented By: Arindam Bhattacharya ( 10305002) Akshat Malu (10305012) Yogesh Kakde (10305039) Tanmay Haldankar ( 10305911). Overview. Introduction

498 views • 36 slides

Machine Learning

Chapter 5. Machine Learning. Learning. 1. Rote learning rote (???) n. ???????,???????,??????????????, (by rote ????????????), ??????????????????? S. repetition 2. Learning by taking advice 3. Learning by problem solving Parameter adjustment Macro-Operators 4. Learning from examples

657 views • 34 slides

Learning To Predict rare events in event sequences

Learning To Predict rare events in event sequences. By Gary M Weiss and Haym Hirsh Presented by Veena Raja Rathna. Contents. Aim & Introduction to the problem Basic problem Formulation Definitions & Evaluation metrics Learning methods

597 views • 32 slides

Query Operations

Query Operations. J. H. Wang Mar. 26, 2008. Text. User Interface. 4, 10. user need. Text. Text Operations. 6, 7. logical view. logical view. Query Operations. DB Manager Module. Indexing. user feedback. 5. 8. inverted file. query. Searching. Index. 8. retrieved docs.

403 views • 30 slides

Geoscience Knowledge Representation Using the SWEET Ontologies

Geoscience Knowledge Representation Using the SWEET Ontologies. Rob Raskin Jet Propulsion Laboratory. Transforming Data into Knowledge. Data Information Knowledge. Basic Elements Bytes Numbers Models Facts

437 views • 29 slides

Paul Smits, Anders Friis-Christensen European Commission, DG Joint Research Centre

Automatic Concept Space Generation in Support of Resource Discovery in Spatial Data Infrastructures. Paul Smits, Anders Friis-Christensen European Commission, DG Joint Research Centre Institute for Environment and Sustainability Spatial Data Infrastructures Unit TP 262, Ispra (VA), Italy.

435 views • 27 slides

Chapter 5: Query Operations

Chapter 5: Query Operations. Baeza-Yates, 1999 Modern Information Retrieval. Query Modification. Improving initial query formulation Relevance feedback approaches based on feedback information from users Local analysis

446 views • 27 slides

Semantic Web for Earth and Environmental Terminology (SWEET) Rob Raskin NASA/JPL July 20, 2006

Semantic Web for Earth and Environmental Terminology (SWEET) Rob Raskin NASA/JPL July 20, 2006. Outline. Why use ontologies? SWEET ontologies Update/community processes. Why Use Ontologies?. Semantic Understanding is Difficult!. Sea surface temperature: measured 3 m above surface

356 views • 26 slides

Lecture 14

Lecture 14. Learning Inductive inference Probably approximately correct learning. What is learning?. Key point: all learning can be seen as learning the representation of a function. Will become clearer with more examples! Example representations: propositional if-then rules

626 views • 25 slides

Concept Space Construction

Concept Space Construction. Todd Littell 27SEP06. Roadmap. Last Year: High quality concept space construction performed offline. First version of inference engine via graph operations. This Year: High quality concept space construction performed online.

419 views • 25 slides

BeeSpace: An Interactive Environment for Analyzing Nature and Nurture in Societal Roles

BeeSpace: An Interactive Environment for Analyzing Nature and Nurture in Societal Roles. Bruce Schatz Institute for Genomic Biology University of Illinois at Urbana-Champaign www.beespace.uiuc.edu FIBR Program Review June 1, 2006 BeeSpace Meetings Spring 2007 -- Jan 17, 2007.

397 views • 25 slides

CS 430: Information Discovery

321 views • 22 slides

What is missing?

327 views • 21 slides

Figure 9.1: A general model of the learning process.

283 views • 17 slides

Modern information retreival

Modern information retreival. Chapter. 02: Modeling (Latent Semantic Indexing). Latent Semantic Indexing. Classic IR might lead to poor retrieval due to: unrelated documents might be included in the answer set relevant documents that do not contain at least one index term are not retrieved

246 views • 14 slides

Modern information retreival

Modern information retreival. Chapter. 02: Modeling (Latent Semantic Indexing). Latent Semantic Indexing. Classic IR might lead to poor retrieval due to: unrelated documents might be included in the answer set relevant documents that do not contain at least one index term are not retrieved

229 views • 14 slides

Network Theory Implications In Air Transportation Systems

Network Theory Implications In Air Transportation Systems. Dr. Bruce J. Holmes, NASA Digital Avionics Systems Conference, Indianapolis October 15, 2003. Outline. Air Transportation Transformation Concept Space A Proposed Air Transportation Network Topology

250 views • 11 slides