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Lazy Learning

Overview of attention for book
Cover of 'Lazy Learning'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Editorial
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    Chapter 2 Locally Weighted Learning
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    Chapter 3 Locally Weighted Learning for Control
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    Chapter 4 Voting over Multiple Condensed Nearest Neighbors
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    Chapter 5 Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching
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    Chapter 6 Discretisation in Lazy Learning Algorithms
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    Chapter 7 Intelligent Selection of Instances for Prediction Functions in Lazy Learning Algorithms
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    Chapter 8 The Racing Algorithm: Model Selection for Lazy Learners
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    Chapter 9 Context-Sensitive Feature Selection for Lazy Learners
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    Chapter 10 Computing Optimal Attribute Weight Settings for Nearest Neighbor Algorithms
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    Chapter 11 A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms
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    Chapter 12 Lazy Acquisition of Place Knowledge
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    Chapter 13 A Teaching Strategy for Memory-Based Control
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    Chapter 14 Lazy Incremental Learning of Control Knowledge for Efficiently Obtaining Quality Plans
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    Chapter 15 IGTree: Using Trees for Compression and Classification in Lazy Learning Algorithms
Attention for Chapter 4: Voting over Multiple Condensed Nearest Neighbors
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Citations

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Summary Dimensions citations
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Chapter title
Voting over Multiple Condensed Nearest Neighbors
Chapter number 4
Book title
Lazy Learning
Published by
Springer, Dordrecht, January 1997
DOI 10.1007/978-94-017-2053-3_4
Book ISBNs
978-9-04-814860-8, 978-9-40-172053-3
Authors

Ethem Alpaydin, Alpaydin, Ethem

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Malaysia 1 2%
Cuba 1 2%
India 1 2%
United Kingdom 1 2%
Belgium 1 2%
Unknown 49 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 19%
Student > Master 8 15%
Researcher 6 11%
Student > Doctoral Student 3 6%
Professor > Associate Professor 3 6%
Other 9 17%
Unknown 15 28%
Readers by discipline Count As %
Computer Science 26 48%
Engineering 4 7%
Mathematics 1 2%
Linguistics 1 2%
Economics, Econometrics and Finance 1 2%
Other 3 6%
Unknown 18 33%

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