'Inductive logic programming' presentation slideshows

Discriminative Learning for Markov Logic Networks

Discriminative Learning for Markov Logic Networks. PhD Proposal. October 9 th , 2009. Tuyen N. Huynh Adviser: Prof. Raymond J. Mooney. Some slides are taken from [Domingos, 2007], [Mooney, 2008]. Motivation.

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Learning Semantic Parsers: An Important But Under-Studied Problem

Learning Semantic Parsers: An Important But Under-Studied Problem. Raymond J. Mooney Dept. of Computer Sciences University of Texas at Austin.

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CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Instructor: Prof. Selman selman@cs.cornell.edu Introduction (Reading R&N: Chapter 1). Course Administration. Office hours and web page by Monday Text book: Russell & Norvig --- Artificial Intelligence: A Modern Approach (AIMA).

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Modeling and Understanding Stress Response Mechanisms with Expresso

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Lecture 11: Graph Data Mining

Lecture 11: Graph Data Mining. Slides are modified from Jiawei Han & Micheline Kamber. Graph Data Mining. DNA sequence RNA. Graph Data Mining. Compounds Texts. Outline. Graph Pattern Mining Mining Frequent Subgraph Patterns Graph Indexing Graph Similarity Search

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Knowledge Representation

Knowledge Representation. Outline: Output - Knowledge representation. Decision tables Decision trees Decision rules Rules involving relations Instance-based representation Prototypes, Clusters. witten&eibe. Output: representing structural patterns.

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Knowledge Representation

Knowledge Representation. Outline: Output - Knowledge representation. Decision tables Decision trees Decision rules Rules involving relations Instance-based representation Prototypes, Clusters. witten&eibe. Output: representing structural patterns.

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Rule-Based Expert Systems

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E-Learning Applications in the EELA Project

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Knowledge Representation

Knowledge Representation. Outline: Output - Knowledge representation. Decision tables Decision trees Decision rules Rules involving relations Instance-based representation Prototypes, Clusters. witten&eibe. Output: representing structural patterns.

415 views • 29 slides

The Power of Microarray Technology

The Power of Microarray Technology. Ruth G. Alscher. Gene Expression: Control Points. Responses to Environmental Signals. Glycolysis, Citric Acid Cycle, and Related Metabolic Processes. Free Radicals Attack Cells: Survival Mechanisms? . ROS Arise as a Result of Exposure to:. Ozone

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Solving ILP Problems in the EELA infrastructure

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Co-inductive logic programming and its applications

Co-inductive logic programming and its applications. Overview. Induction vs. co-induction Co-inductive logic programming A goal-directed approach for Answer Set Computing. Induction. Inductive definitions have 3 components: Initiality (e.g., [] is a list)

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Logic programming

Logic programming. Combining declarative and procedural representations: Emergencies on the London Underground Logic programming for proactive rather than reactive behaviour: The fox and the crow Two classes of semantics: if-and-only-if versus minimal model semantics

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Predicate Calculus Syntax

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Gunnar AAstrand Grimnes Alun Preece & Pete Edwards University of Aberdeen ggrimnes@csd.abdn.ac.uk

11/4/2002. Personal Agents - The impact of the Semantic Web First presented at AgentLink II - Amsterdam, December 2001. Gunnar AAstrand Grimnes Alun Preece & Pete Edwards University of Aberdeen ggrimnes@csd.abdn.ac.uk. Challenges: Representation of training events. Data volume.

243 views • 17 slides

Wednesday, February 7, 2001 William H. Hsu Department of Computing and Information Sciences, KSU

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Learning Sets of Rules

Learning Sets of Rules. Learning Rules. If (Color = Red) and (Shape = round) then Class is A If (Color = Blue) and (Size = large) then Class is B If (Shape = square) then Class is A Natural and intuitive hypotheses Comprehensibility - Easy to understand?

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Supervised Learning

Supervised Learning. The supervisor: Perrin Westrich. Outline. What is supervised learning? What is it used for? Approaches. What is supervised learning?. A machine learning technique Training data Validation. Approaches. Neural networks Decision trees Inductive Logic Programming.

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Disulfide bonds prediction using ILP

Abstract

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