'Semantic concepts' presentation slideshows

14.0 Linguistic Processing and Latent Topic Analysis

???????? Introduction to Digital Speech Processing. 14.0 Linguistic Processing and Latent Topic Analysis. ? ???????????? ?? CC ???????????????????? 3.0 ? ???? ?. ??????????? ?????? ??? ??. Latent Semantic Analysis (LSA ). T k. Topic. Documents. Words.

344 views • 34 slides

Driving Deep Semantics in Middleware and Networks: What, why and how?

Driving Deep Semantics in Middleware and Networks: What, why and how?. Amit Sheth Keynote @ Semantic Sensor Networks Workshop @ ISWC2006 November 06, 2006, Athens GA Thanks: Doug Brewer, Lakshmish Ramaswamy. SW Today. Can create large populated ontologies

582 views • 33 slides

Driving Deep Semantics in Middleware and Networks: What, why and how?

Driving Deep Semantics in Middleware and Networks: What, why and how?. Amit Sheth Keynote @ Semantic Sensor Networks Workshop @ ISWC2006 November 06, 2006, Athens GA Thanks: Doug Brewer, Lakshmish Ramaswamy. SW Today. Can create large populated ontologies

472 views • 33 slides

14.0 Linguistic Processing and Latent Topic Analysis

14.0 Linguistic Processing and Latent Topic Analysis. Latent Semantic Analysis (LSA ). T k. Topic. Words. Documents. d 1 d 2 ........ d j .......... d N. w 1 w 2 w i w M. w ij. Latent Semantic Analysis (LSA) - Word-Document Matrix Representation.

295 views • 29 slides

Training Image Classifiers with Similarity Metrics, Linear Programming, and Minimal Supervision

Training Image Classifiers with Similarity Metrics, Linear Programming, and Minimal Supervision. Asilomar SSC Karl Ni, Ethan Phelps, Katherine Bouman, Nadya Bliss Lincoln Laboratory, Massachusetts Institute of Technology 2 November 2012.

409 views • 28 slides

Concept-based Image and Video Retrieval

596 views • 27 slides

What Are the High-Level Concepts with Small Semantic Gaps?

What Are the High-Level Concepts with Small Semantic Gaps?. CS 4763 Multimedia System, Spring 2008. Outline. Introduction LCSS: a lexicon of high-level concepts with small semantic gaps. Framework of LCSS Construction Methodologies Experimental results Conclusion. Introduction.

346 views • 25 slides

Jianping Fan, Yuli Gao, Hangzai Luo, Guangyou Xu SIGIR 2004

Automatic Image Annotation by Using Concept-Sensitive Salient Objects for Image Content Representation. Jianping Fan, Yuli Gao, Hangzai Luo, Guangyou Xu SIGIR 2004. Abstract.

366 views • 24 slides

Learning to Share Meaning in a Multi-Agent System (Part I)

Learning to Share Meaning in a Multi-Agent System (Part I). Ganesh Padmanabhan. Article. Williams, A.B. , "Learning to Share Meaning in a Multi-Agent System " , Journal of Autonomous Agents and Multi-Agent Systems , Vol. 8, No. 2, 165-193, March 2004. (Most downloaded article in Journal).

324 views • 22 slides

228 views • 22 slides

14.1 Introduction

14.1 Introduction. Database systems typically have only a very limited understanding of what the data in the database means. Semantic modeling ideas can be useful as an aid in the process of database design, even in the absence of direct DBMS support for those ideas.

190 views • 19 slides

Experience Building The World Wide Telescope aka: The Virtual Observatory

Experience Building The World Wide Telescope aka: The Virtual Observatory. Jim Gray Alex Szalay. The Evolution of Science. Observational Science Scientist gathers data by direct observation Scientist analyzes data Analytical Science Scientist builds analytical model Makes predictions.

328 views • 18 slides