'Version space' diaporamas de présentation

Second Order Learning

Second Order Learning. Koby Crammer Department of Electrical Engineering. ECML PKDD 2013 Prague. Thanks. Mark Dredze Alex Kulesza Avihai Mejer Edward Moroshko Francesco Orabona Fernando Pereira Yoram Singer Nina Vaitz. Tutorial Context. Online

1.29k views • 114 slides

A tutorial on rule induction

A tutorial on rule induction. Peter A. Flach Department of Computer Science University of Bristol www.cs.bris.ac.uk/~flach/. Overview. Introduction Learning rules with CN2 Learning Prolog rules with ILP Rule learning with other declarative languages. Example 1: linear classification.

1.03k views • 73 slides

Concept learning

Concept learning. Maria Simi, 2012/2013 Machine Learning, Tom Mitchell Mc Graw -Hill International Editions, 1997 ( Cap 1, 2). Introduction to machine learning. Introduction to machine learning When appropriate and when not appropriate Task definition

1.23k views • 50 slides

Concept learning

Concept learning. Maria Simi, 2011/2012 Machine Learning, Tom Mitchell Mc Graw -Hill International Editions, 1997 ( Cap 1, 2). Introduction to machine learning. Introduction to machine learning When appropriate and when not appropriate Task definition

880 views • 50 slides

Inductive Learning (continued)

Inductive Learning (continued). Chapter 19. Slides for Ch. 19 by J.C. Latombe. Learning logical descriptions. The process of constructing a decision tree can be seen as searching the hypothesis space H. The goal is to construct an hypothesis H that explains the data in the training set.

635 views • 47 slides

Knowledge in Learning (Logical formulation of Learning) CS570 Lecture Notes by Jin Hyung Kim

Knowledge in Learning (Logical formulation of Learning) CS570 Lecture Notes by Jin Hyung Kim Computer Science Department KAIST. Leaning General Logical Descriptions. Find ( general ) logical descriptions consistent with sample data (examples)

2.05k views • 46 slides

Concept learning

Concept learning. Maria Simi, 2010/2011. Plan. Introduction to machine learning Task definition When appropriate and when not appropriate Learning methodology: design, experiment, evaluation Learning issues: representing hypothesis Taxonomy of learning models

766 views • 46 slides

Active Learning: Class Questions

567 views • 39 slides

Concept Learnin g

Concept Learnin g. C.C. Fang Reference: Mitchell Machine Learning, Chapter 2. Outline. Concept learning Learning from instances (examples) General-to-specific ordering over hypotheses Version spaces and candidate-elimination algorithm The role of bias. The definition of Concept Learning.

710 views • 35 slides

Reasoning with Multi-version Ontologies: a temporal logic approach

Reasoning with Multi-version Ontologies: a temporal logic approach. Zhisheng Huang and Heiner Stuckenschmidt Vrije University Amsterdam The Netherlands (ISWC05 paper). Outline. Motivation Framework Temporal logics Examples Prototype of MORE (Multi-version Ontology REasoner)

492 views • 31 slides

2. Concept Learning

1.2k views • 31 slides

Learning From Observation Part II

Learning From Observation Part II. KAIST Computer Science 20013221 ? ? ?. Contents. Using Information Theory Learning General Logical Descriptions. Using Information Theory. 1. Introduction 2. Noise and Over-fitting 3. Issues related with the decision tree. Introduction.

374 views • 30 slides

Chapter 10 Machine Learning: Symbol-Based

Chapter 10 Machine Learning: Symbol-Based. Contents. A Framework Version Space Search ID3: Decision Tree. Machine Learning. AI systems grow from a minimal amount of knowledge by learning Herbert Simon (1983):

832 views • 30 slides

Dynamic Aspects of SEKT Legal Ontologies

Dynamic Aspects of SEKT Legal Ontologies. Zhisheng Huang, Stefan Schlobach, Frank van Harmelen Vrije University Amsterdam Nuria Casellas and Pompeu Casanovas Universitat Autonoma de Barcelona. Outline. SEKT Legal Ontologies Ontology Versioning: Motivation

374 views • 26 slides

C ONTENT-ORIENTED NEGOTIATION IN E-C OMMERCE

409 views • 26 slides

Artificial Intelligence 6. Machine Learning, Version Space Method

Artificial Intelligence 6. Machine Learning, Version Space Method. Japan Advanced Institute of Science and Technology (JAIST) Yoshimasa Tsuruoka. Outline. Introduction to machine learning What is machine learning? Applications of machine learning Version space method

608 views • 25 slides

Semi-Automatic Learning of Transfer Rules for Machine Translation of Low-Density Languages

Semi-Automatic Learning of Transfer Rules for Machine Translation of Low-Density Languages. Katharina Probst April 5, 2002. Overview of the talk. Introduction and Motivation Overview of the AVENUE project Elicitation of bilingual data Rule Learning Seed Generation

379 views • 23 slides

Lecture 01 of 42

Lecture 01 of 42. Introduction to Machine Learning and the Version Space Formalism. Wednesday, 24 January 2008 William H. Hsu Department of Computing and Information Sciences, KSU KSOL course pages: http://snipurl.com/1y5gc / http://snipurl.com/1y5ih

290 views • 20 slides

Monday, January 22, 2001 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 3. Data Mining Basics. Monday, January 22, 2001 William H. Hsu Department of Computing and Information Sciences, KSU http://www.cis.ksu.edu/~bhsu Readings: Chapter 1-2, Witten and Frank Sections 2.7-2.8, Mitchell. Lecture Outline.

328 views • 18 slides

Figure 9.1: A general model of the learning process.

284 views • 17 slides