'Probabilistic model' presentation slideshows

?????? : ???? Statistical Language Models for IR

?????? : ???? Statistical Language Models for IR. ChengXiang Zhai (???) Department of Computer Science Graduate School of Library & Information Science Institute for Genomic Biology, Statistics University of Illinois, Urbana-Champaign

984 views • 80 slides

Learning to Classify Text

Learning to Classify Text. William W. Cohen Center for Automated Learning and Discovery Carnegie Mellon University. Outline. Some examples of text classification problems topical classification vs genre classification vs sentiment detection vs authorship attribution vs ...

763 views • 64 slides

Bayesian Methods with Monte Carlo Markov Chains I

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Lecture 3: Descriptive Data Mining Peter van der Putten (putten_at_liacs.nl)

Lecture 3: Descriptive Data Mining Peter van der Putten (putten_at_liacs.nl). Course Outline. Objective Understand the basics of data mining Gain understanding of the potential for applying it in the bioinformatics domain Hands on experience Schedule Evaluation

848 views • 61 slides

Human Pose Recognition

Human Pose Recognition. Contents. Introduction Article [1] Real Time Motion Capture Using a Single TOF Camera (2010) Article [2] Real Time Human Pose Recognition In Parts Using a Single Depth Images(2011). 1.1 What Is Pose Recognition?. head. Input Image. torso. arm. Fig From [2].

785 views • 54 slides

Chapter 2 Modeling

Chapter 2 Modeling. Modern Information Retrieval by R. Baeza-Yates and B. Ribeiro-Neto. Introduction. Traditional information retrieval systems usually adopt index terms to index and retrieve documents.

894 views • 45 slides

Gene Prediction: Similarity-Based Approaches

Gene Prediction: Similarity-Based Approaches. Outline. The idea of similarity-based approach to gene prediction Exon Chaining Problem Spliced Alignment Problem Gene prediction tools. Using Known Genes to Predict New Genes.

712 views • 44 slides

Scientific Data Mining

Scientific Data Mining. Principles and applications with astronomical data. Amos Storkey Institute for Adaptive and Neural Computation Division of Informatics and Institute for Astronomy University of Edinburgh. Collaborators and Thanks.

437 views • 32 slides

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Logistics

Logistics. Course reviews Project report deadline: March 16 Poster session guidelines: 2.5 minutes per poster (3 hrs / 55 minus overhead) presentations will be videotaped food will be provided ?. Task: Named-Entity Recognition in new corpus. Named-Entity Recognition.

426 views • 26 slides

Large -Scale Cost-sensitive Online Social Network Profile Linkage

Large -Scale Cost-sensitive Online Social Network Profile Linkage. Background & Motivation. Foot prints in different social networks. User identification in social analysis. Privacy & security Commercial & government applications. Outline. Problem definition Related work Approach

338 views • 23 slides

A fast local descriptor for dense matching

A fast local descriptor for dense matching. Engin Tola, Vincent Lepetit, Pascal Fua Ecole Polytechnique Federale de Lausanne, Switzerland. Wide baseline stereo . Stereo is called wide baseline if there are significant rotations of the camera translation between camera centers

460 views • 21 slides

Probabilistic Databases

Probabilistic Databases. Amol Deshpande, University of Maryland. Overview. V.S. Subrahmanian ProbView, PXML, Temporal Probabilistic Databases, Probabilistic Aggregates Lise Getoor Statistical Relational Learning, Probabilistic Relational Models, Entity Resolution Amol

363 views • 18 slides

How Many Words is a Picture Worth? Automatic Caption Generation for News Images

How Many Words is a Picture Worth? Automatic Caption Generation for News Images. Yansong Feng and Mirella Lapata. Ashish Bagate. What this paper is about. Explore the feasibility of automatic caption generation for images in news domain

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Bayesian Sets

486 views • 15 slides

Tree Reconstruction

236 views • 13 slides

Learning to Extract Symbolic Knowledge from the World Wide Web

Learning to Extract Symbolic Knowledge from the World Wide Web. Changho Choi Source: http://www.cs.cmu.edu/~knigam/ Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum Carnegie Mellon University, J.Stefan Institute AAAI-98. Abstract. Information on the Web. Unstandable to Human.

508 views • 12 slides

TESSA - a system to aid communication with deaf people

200 views • 9 slides

DBMS with probabilistic model

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Probabilistic Models

Probabilistic Models. Consider our FMR triplet repeat finite state automaton. a. g. c. g. c. g. g. c. t. g. e. S. 1. 2. 3. 4. 5. 6. 7. 8. c. While the above finite state automaton above is non-deterministic , it is NOT probabilistic .

151 views • 0 slides