'Rough sets' diaporamas de présentation

Granular Computing for Machine Learning

Granular Computing for Machine Learning. JingTao Yao Department of Computer Science, University of Regina jtyao@cs.uregina.ca http://www.cs.uregina.ca/~jtyao. Granular Computing. Granular Computing (GrC)

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Rough Sets Tutorial

Rough Sets Tutorial. Contents. Introduction Basic Concepts of Rough Sets Concluding Remarks (Summary, Advanced Topics, References and Further Readings). This is an abridged version of a ppt of 208 slides!!. Introduction.

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Overview of Rough Sets

Overview of Rough Sets. Rough Sets (Theoretical Aspects of Reasoning about Data) by Zdzislaw Pawlak. Contents. Introduction Basic concepts of Rough Sets information system equivalence relation / equivalence class / indiscernibility relation

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Personalizing Information Retrieval in CRISs with Fuzzy Sets and Rough Sets

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Personalizing Information Retrieval in CRISs with Fuzzy Sets and Rough Sets

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More Rough Sets Various Reducts and Rough Sets Applications

More Rough Sets Various Reducts and Rough Sets Applications. Rough Sets - Reducts Algorithms for computing reducts or reduct approximations are discussed following. Note that any attribute subset is in this context considered to be an approximation to a reduct.

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More Rough Sets

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Improving Support Vector Machine through Parameter Optimized

Rujiang Bai, Junhua Liao Shandong University of Technology Library Zibo 255049, China { brj, ljhbrj}@sdut.edu.cn. Improving Support Vector Machine through Parameter Optimized. Outline. Motivation and Background RGSC(Rough sets and Genetic algorithms for SVM classifier) Experiment

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chap 2: imprecise categories, approximations and rough sets

chap 2: imprecise categories, approximations and rough sets. Presented by Farzana Forhad & Xiaqing he. OUTLINE. Rough Sets Approximations of Set Properties of Approximations Approximations and Membership Relation Numerical Characterization of Imprecision

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Properties of Approximations

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A Granular Computing Paradigm for Concept Learning

A Granular Computing Paradigm for Concept Learning. Xiaofei Deng Ph.D. Candidate Department of Computer Science. Outline. Concept learning Granular computing A model for concept learning Applications. What is concept learning?. What is a concept? Basic unit of human thought

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Mining Time Series Using Rough Sets - a Case Study

Mining Time Series Using Rough Sets - a Case Study. Anders Torvill Bjorvand Troll Data Inc. torvill@trolldata.no. Objective. We want to reason about sequences of events Including historic attributes increases the number of attributes initially

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INTRODUCTION TO COMPUTATIONAL INTELLIGENCE

INTRODUCTION TO COMPUTATIONAL INTELLIGENCE . Lin Shang Dept . of Computer Science and Technology Nanjing University. People. Lecturer : SHANG Lin ( shanglin@nju.edu.cn ) TA: LIU Xing () XIE Cheng () Course URL: http://cs.nju.edu.cn/shanglin/ci Time: Thu. 2:00-4:00 pm

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3. Rough set extensions

3. Rough set extensions In the rough set literature, several extensions have been developed that attempt to handle better the uncertainty present in real world data.

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Summary

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Summary

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