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Ontologies and Concepts in Mind and Machine

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Cover of 'Ontologies and Concepts in Mind and Machine'

Table of Contents

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    Book Overview
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    Chapter 1 A Formalism Unifying Defeasible Logics and Repair Semantics for Existential Rules
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    Chapter 2 Using Grammar-Based Genetic Programming for Mining Disjointness Axioms Involving Complex Class Expressions
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    Chapter 3 An Incremental Algorithm for Computing All Repairs in Inconsistent Knowledge Bases
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    Chapter 4 Knowledge-Based Matching of n -ary Tuples
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    Chapter 5 Some Programming Optimizations for Computing Formal Concepts
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    Chapter 6 Preventing Overlaps in Agglomerative Hierarchical Conceptual Clustering
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    Chapter 7 Interpretable Concept-Based Classification with Shapley Values
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    Chapter 8 Pruning in Map-Reduce Style CbO Algorithms
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    Chapter 9 Pattern Discovery in Triadic Contexts
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    Chapter 10 Characterizing Movie Genres Using Formal Concept Analysis
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    Chapter 11 Restricting the Maximum Number of Actions for Decision Support Under Uncertainty
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    Chapter 12 Vocabulary-Based Method for Quantifying Controversy in Social Media
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    Chapter 13 Multi-label Learning with a Cone-Based Geometric Model
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    Chapter 14 Conceptual Reasoning for Generating Automated Psychotherapeutic Responses
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    Chapter 15 Benchmarking Inference Algorithms for Probabilistic Relational Models
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    Chapter 16 Analyzing Psychological Similarity Spaces for Shapes
Attention for Chapter 15: Benchmarking Inference Algorithms for Probabilistic Relational Models
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Chapter title
Benchmarking Inference Algorithms for Probabilistic Relational Models
Chapter number 15
Book title
Ontologies and Concepts in Mind and Machine
Published by
Springer, Cham, September 2020
DOI 10.1007/978-3-030-57855-8_15
Book ISBNs
978-3-03-057854-1, 978-3-03-057855-8
Authors

Tristan Potten, Tanya Braun

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Student > Bachelor 1 50%
Readers by discipline Count As %
Computer Science 2 100%