'Domain theory' diaporamas de présentation

Domain Theory, Computational Geometry and Differential Calculus

Domain Theory, Computational Geometry and Differential Calculus. Abbas Edalat Imperial College London www.doc.ic.ac.uk/~ae With contributions from Andre Lieutier, Ali Khanban, Marko Krznaric and Dirk Pattinson First SQU Workshop on Topology and its Applications December 2004.

1.45k views • 127 slides

Towards a Unifying Logic-Based Framework ( TUF ) for Computing

Towards a Unifying Logic-Based Framework ( TUF ) for Computing. Robert Kowalski Imperial College London joint work with Fariba Sadri (There is a paper with a similar title on my webpage.). Towards a Unifying Logic-Based Framework ( TUF ). The Computer Science Zoo

829 views • 51 slides

Update on RKF progress October, 2000

Update on RKF progress October, 2000. Ken Forbus Qualitative Reasoning Group Northwestern University. Overview. Analogical Reasoning Reasoning Engines Domain Theories Sketching. Base. SME. CVmatch. SME. CVmatch. Target. SME. CVmatch. SME. CVmatch. Our analogical processing tools.

607 views • 51 slides

Scott Closed Set Lattices And Applications

Scott Closed Set Lattices And Applications. 1. Some preliminaries 2. Dcpo-completion 3. Equivalence between CONP and CDL 4. The Hoare power domain 5. Scott closed set lattices of complete semilattices 6. Some problems and remarks for further research. 1. Preliminaries.

587 views • 44 slides

Knowledge Acquisition

Knowledge Acquisition. Components of KA. 1. Expert a person who solves complex problems. May not be aware of knowledge representation. Expresses his action in his own way of solving problems. 2. Knowledge Engineer Well acquainted with techniques of computer science.

411 views • 40 slides

Complexity of Classical Planning

Complexity of Classical Planning. Megan Smith Lehigh University CSE 497, Spring 2007. Outline. Review Classical Planning Complexity Background Computational Complexity Main Results. Review: Classical Representation. Function-free first-order language L

693 views • 33 slides

Analytical Learning

Analytical Learning. Introduction Learning with Perfect Domain Theories Explanation-Based Learning Search Control Knowledge Summary. Introduction. Learning algorithms like neural networks, decision trees, inductive logic programming, etc. all require a good number of examples to

1.1k views • 26 slides

Explanation-Based Learning (EBL)

Explanation-Based Learning (EBL). One definition: Learning general problem-solving techniques by observing and analyzing human solutions to specific problems. The EBL Hypothesis. By understanding why an example is a member of a concept, can learn the essential properties of the concept

1.24k views • 24 slides

Explanation-Based Learning (EBL)

Explanation-Based Learning (EBL). One definition: Learning general problem-solving techniques by observing and analyzing human solutions to specific problems. The EBL Hypothesis. By understanding why an example is a member of a concept, can learn the essential properties of the concept

302 views • 24 slides

Combining Inductive and Analytical Learning

Combining Inductive and Analytical Learning. Why combine inductive and analytical learning? KBANN: prior knowledge to initialize the hypothesis TangentProp, EBNN: prior knowledge alters search objective FOCL: prior knowledge alters search operators. Inductive and Analytical Learning.

1.05k views • 19 slides

Machine Learning Chapter 12. Combining Inductive and Analytical Learning

Machine Learning Chapter 12. Combining Inductive and Analytical Learning. Tom M. Mitchell. Inductive and Analytical Learning. Inductive learning Hypothesis fits data Statistical inference Requires little prior knowledge Syntactic inductive bias. Analytical learning

943 views • 17 slides

Becky Gomez #0197933 March 3,2013

EDTC 3320 Instructional Design Project 2 Learning Theories Presentations Professor Matthew Crosslin. Becky Gomez #0197933 March 3,2013. Introduction. This PowerPoint presentation is about : Benjamin Bloom and his Taxonomy Cognitive domain theory

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Combining Inductive and Analytical Learning

Combining Inductive and Analytical Learning. Ch 12. in Machine Learning Tom M. Mitchell ????? ????? ??? ? ? ? 1999. 7. 9. Contents. Motivation Inductive-Analytical Approaches to Learning Using Prior Knowledge to Initialize the Hypothesis The KBANN Algorithm

1.18k views • 16 slides

Analytical Learning

Analytical Learning. Introduction Learning with Perfect Domain Theories Explanation-Based Learning Search Control Knowledge Summary. Discovering New Features. The Prolog-EBG system we described before can formulate new features that do not show up in the examples.

414 views • 16 slides

Analytical Learning

Analytical Learning. Introduction Learning with Perfect Domain Theories Explanation-Based Learning Search Control Knowledge Summary. Discovering New Features. The Prolog-EBG system we described before can formulate new features that do not show up in the examples.

277 views • 16 slides

CS 9633 Machine Learning Inductive-Analytical Methods

CS 9633 Machine Learning Inductive-Analytical Methods. Inductive and Analytical Methods. Inductive methods seek general hypotheses that fit observed training data. Can fail with insufficient data May be misled by incorrect bias

317 views • 13 slides

Explanation Based Learning (EBL)

Explanation Based Learning (EBL). By M. Muztaba Fuad. What is EBL ?. Learning general problem-solving techniques by observing and analyzing human solutions to specific problems. EBL attempts to formulate a generalization after observing only a single example.

234 views • 11 slides

Elliot Turiel

97 views • 9 slides