'Relational models' diaporamas de présentation

- Relational - Graphical Models

MINVOLSET. KINKEDTUBE. PULMEMBOLUS. INTUBATION. VENTMACH. DISCONNECT. PAP. SHUNT. VENTLUNG. VENITUBE. PRESS. MINOVL. FIO2. VENTALV. PVSAT. ANAPHYLAXIS. ARTCO2. EXPCO2. SAO2. TPR. INSUFFANESTH. HYPOVOLEMIA. LVFAILURE. CATECHOL. LVEDVOLUME. STROEVOLUME. ERRCAUTER. HR.

884 views • 76 slides

- Relational - Graphical Models

MINVOLSET. KINKEDTUBE. PULMEMBOLUS. INTUBATION. VENTMACH. DISCONNECT. PAP. SHUNT. VENTLUNG. VENITUBE. PRESS. MINOVL. FIO2. VENTALV. PVSAT. ANAPHYLAXIS. ARTCO2. EXPCO2. SAO2. TPR. INSUFFANESTH. HYPOVOLEMIA. LVFAILURE. CATECHOL. LVEDVOLUME. STROEVOLUME. ERRCAUTER. HR.

884 views • 76 slides

Markov Logic: Combining Logic and Probability

Markov Logic: Combining Logic and Probability. Parag Singla Dept. of Computer Science & Engineering Indian Institute of Technology Delhi. Overview. Motivation & Background Markov logic Inference & Learning Abductive Plan Recognition. Social Network and Smoking Behavior. Smoking.

655 views • 65 slides

Markov Logic: Combining Logic and Probability

Markov Logic: Combining Logic and Probability. Parag Singla Dept. of Computer Science & Engineering Indian Institute of Technology Delhi. Overview. Motivation & Background Markov logic Inference & Learning Abductive Plan Recognition. Social Network and Smoking Behavior. Smoking.

654 views • 65 slides

A Quick Romp Through Probabilistic Relational Models

A Quick Romp Through Probabilistic Relational Models. Teg Grenager NLP Lunch February 13, 2003. Agenda. Bayesian Networks Probabilistic Relational Models Learning PRMs Expressivity Applications to NLP. Agenda. Bayesian Networks Probabilistic Relational Models Learning PRMs

692 views • 54 slides

A Quick Romp Through Probabilistic Relational Models

A Quick Romp Through Probabilistic Relational Models. Teg Grenager NLP Lunch February 13, 2003. Agenda. Bayesian Networks Probabilistic Relational Models Learning PRMs Expressivity Applications to NLP. Agenda. Bayesian Networks Probabilistic Relational Models Learning PRMs

544 views • 54 slides

Learning Probabilistic Relational Models

Learning Probabilistic Relational Models. Lise Getoor 1 , Nir Friedman 2 , Daphne Koller 1 , and Avi Pfeffer 3 1 Stanford University, 2 Hebrew University, 3 Harvard University. Overview. Motivation Definitions and semantics of probabilistic relational models (PRMs) Learning PRMs from data

625 views • 52 slides

Probabilistic Models of Object-Relational Domains

Probabilistic Models of Object-Relational Domains. Daphne Koller Stanford University Joint work with:. Lise Getoor. Ben Taskar. Drago Anguelov. Pieter Abbeel. Rahul Biswas. Nir Friedman. Ming-Fai Wong. Evan Parker. Avi Pfeffer. Eran Segal. Intell_Jane. Diffic_CS101. Intell_George.

629 views • 51 slides

GATE Frameworks

411 views • 30 slides

492 views • 27 slides

Learning to Coordinate: A Relational Model of Organizational Change

Learning to Coordinate: A Relational Model of Organizational Change. Jody Hoffer Gittell, Brandeis University Amy Edmondson, Harvard Business School Edgar Schein, MIT Sloan School of Management 2011 Academy of Management San Antonio, TX. Learning to coordinate.

358 views • 23 slides

Learning to Coordinate: A Relational Model of Organizational Change

Learning to Coordinate: A Relational Model of Organizational Change. Jody Hoffer Gittell, Brandeis University Amy Edmondson, Harvard Business School Edgar Schein, MIT Sloan School of Management 2011 Academy of Management San Antonio, TX. Learning to coordinate.

445 views • 23 slides

China and Taiwan GATE Class Activities

235 views • 22 slides

Object-Database

400 views • 21 slides

Bayesian Network Tools in Java (BNJ) v2.0

Bayesian Network Tools in Java (BNJ) v2.0. William H. Hsu Other Contributors Roby Joehanes Prashanth Boddhireddy Haipeng Guo Siddharth Chandak Benjamin B. Perry Charles Thornton Julie A. Thornton http://bndev.sourceforge.net. What is BNJ?.

300 views • 21 slides

Spring 2011 Instructor: Hassan Khosravi

The Semistructure d -Data Model Programming Languages for XML. Spring 2011 Instructor: Hassan Khosravi. Semistructured Data. Another data model, based on trees. Self-describing : The data implicitly carries information about what its schema is.

280 views • 17 slides

CSET 3300: Database-Driven Web Applications

CSET 3300: Database-Driven Web Applications. Spring 2010 William Acosta Email : william.acosta@utoledo.edu URL: http://cset.sp.utoledo.edu/~wacosta / spring 2010/cset3300. Introduction. Instructor William Acosta 1624 North Engineering 419.530.3896 william.acosta @ utoledo.edu

228 views • 14 slides