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Militarized Conflict Modeling Using Computational Intelligence

Overview of attention for book
Cover of 'Militarized Conflict Modeling Using Computational Intelligence'

Table of Contents

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    Book Overview
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    Chapter 1 Modeling Conflicts Between States: New Developments for an Old Problem
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    Chapter 2 Automatic Relevance Determination for Identifying Interstate Conflict
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    Chapter 3 Multi-layer Perceptron and Radial Basis Function for Modeling Interstate Conflict
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    Chapter 4 Bayesian Approaches to Modeling Interstate Conflict
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    Chapter 5 Support Vector Machines for Modeling Interstate Conflict
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    Chapter 6 Fuzzy Sets for Modeling Interstate Conflict
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    Chapter 7 Rough Sets for Modeling Interstate Conflict
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    Chapter 8 Particle Swarm Optimization and Hill-Climbing Optimized Rough Sets for Modeling Interstate Conflict
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    Chapter 9 Simulated Annealing Optimized Rough Sets for Modeling Interstate Conflict
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    Chapter 10 Genetic Algorithm with Optimized Rough Sets for Modeling Interstate Conflict
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    Chapter 11 Neuro-Rough Sets for Modeling Interstate Conflict
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    Chapter 12 Early Warning and Conflict Prevention Using Computational Techniques
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    Chapter 13 Conclusions and Emerging Topics
Attention for Chapter 2: Automatic Relevance Determination for Identifying Interstate Conflict
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Citations

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Chapter title
Automatic Relevance Determination for Identifying Interstate Conflict
Chapter number 2
Book title
Militarized Conflict Modeling Using Computational Intelligence
Published by
Springer, London, January 2011
DOI 10.1007/978-0-85729-790-7_2
Book ISBNs
978-0-85729-789-1, 978-0-85729-790-7
Authors

Tshilidzi Marwala, Monica Lagazio, Marwala, Tshilidzi, Lagazio, Monica

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

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

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