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Algorithmic Learning Theory

Overview of attention for book
Cover of 'Algorithmic Learning Theory'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Extracting Information from the Web for Concept Learning and Collaborative Filtering
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    Chapter 2 The Divide-and-Conquer Manifesto
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    Chapter 3 Sequential Sampling Techniques for Algorithmic Learning Theory
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    Chapter 4 Towards an Algorithmic Statistics
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    Chapter 5 Minimum Message Length Grouping of Ordered Data
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    Chapter 6 Learning From Positive and Unlabeled Examples
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    Chapter 7 Learning Erasing Pattern Languages with Queries
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    Chapter 8 Learning Recursive Concepts with Anomalies
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    Chapter 9 Identification of Function Distinguishable Languages
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    Chapter 10 A Probabilistic Identification Result
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    Chapter 11 A New Framework for Discovering Knowledge from Two-Dimensional Structured Data Using Layout Formal Graph System
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    Chapter 12 Hypotheses Finding via Residue Hypotheses with the Resolution Principle
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    Chapter 13 Conceptual Classifications Guided by a Concept Hierarchy
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    Chapter 14 Learning Taxonomic Relation by Case-based Reasoning
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    Chapter 15 Average-Case Analysis of Classification Algorithms for Boolean Functions and Decision Trees
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    Chapter 16 Self-duality of Bounded Monotone Boolean Functions and Related Problems
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    Chapter 17 Sharper Bounds for the Hardness of Prototype and Feature Selection
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    Chapter 18 On the Hardness of Learning Acyclic Conjunctive Queries
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    Chapter 19 Dynamic Hand Gesture Recognition Based On Randomized Self-Organizing Map Algorithm
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    Chapter 20 On Approximate Learning by Multi-layered Feedforward Circuits
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    Chapter 21 The Last-Step Minimax Algorithm
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    Chapter 22 Rough Sets and Ordinal Classification
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    Chapter 23 A note on the generalization performance of kernel classifiers with margin
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    Chapter 24 On the Noise Model of Support Vector Machines Regression
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    Chapter 25 Computationally Efficient Transductive Machines
Attention for Chapter 11: A New Framework for Discovering Knowledge from Two-Dimensional Structured Data Using Layout Formal Graph System
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Chapter title
A New Framework for Discovering Knowledge from Two-Dimensional Structured Data Using Layout Formal Graph System
Chapter number 11
Book title
Algorithmic Learning Theory
Published by
Springer, Berlin, Heidelberg, December 2000
DOI 10.1007/3-540-40992-0_11
Book ISBNs
978-3-54-041237-3, 978-3-54-040992-2
Authors

Tomoyuki Uchida, Yuko Itokawa, Takayoshi Shoudai, Tetsuhiro Miyahara, Yasuaki Nakamura

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 8%
Italy 1 8%
Czechia 1 8%
China 1 8%
United States 1 8%
Unknown 8 62%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 54%
Researcher 2 15%
Professor 1 8%
Lecturer 1 8%
Student > Master 1 8%
Other 1 8%
Readers by discipline Count As %
Computer Science 11 85%
Engineering 1 8%
Unknown 1 8%