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Advances in Data Analysis with Computational Intelligence Methods

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Cover of 'Advances in Data Analysis with Computational Intelligence Methods'

Table of Contents

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    Book Overview
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    Chapter 1 Tensor Networks for Dimensionality Reduction, Big Data and Deep Learning
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    Chapter 2 Local Data Characteristics in Learning Classifiers from Imbalanced Data
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    Chapter 3 Dimensions of Semantic Similarity
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    Chapter 4 Some Interesting Phenomenon Occurring During Self-learning Process with Its Psychological Interpretation
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    Chapter 5 On the Interpretation and Characterization of Echo State Networks Dynamics: A Complex Systems Perspective
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    Chapter 6 Optimization of Ensemble Neural Networks with Type-1 and Interval Type-2 Fuzzy Integration for Forecasting the Taiwan Stock Exchange
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    Chapter 7 Deep Neural Networks—A Brief History
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    Chapter 8 Techniques for Construction and Integration of Rule Bases
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    Chapter 9 New Aspects of Interpretability of Fuzzy Systems for Nonlinear Modeling
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    Chapter 10 On the Intuitionistic Fuzzy Sets of n-th Type
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    Chapter 11 MCTS/UCT in Solving Real-Life Problems
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    Chapter 12 Interactive Cone Contraction for Evolutionary Mutliple Objective Optimization
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    Chapter 13 A Review of Fuzzy and Mathematic Methods for Dynamic Parameter Adaptation in the Firefly Algorithm
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    Chapter 14 Computational Intelligence Methods in Personalized Pharmacotherapy
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    Chapter 15 Embodying Intelligence in Autonomous and Robotic Systems with the Use of Cognitive Psychology and Motivation Theories
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    Chapter 16 Evolutionary Approach for Automatic Design of PID Controllers
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    Chapter 17 Fuzzy-Genetic Approach to Identity Verification Using a Handwritten Signature
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    Chapter 18 A Method of Design and Optimization for SiC-Based Grid-Connected AC-DC Converters
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Citations

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Title
Advances in Data Analysis with Computational Intelligence Methods
Published by
Springer International Publishing, January 2018
DOI 10.1007/978-3-319-67946-4
ISBNs
978-3-31-967945-7, 978-3-31-967946-4
Editors

Adam E Gawęda, Janusz Kacprzyk, Leszek Rutkowski, Gary G. Yen

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 22%
Student > Doctoral Student 2 22%
Lecturer 1 11%
Other 1 11%
Student > Bachelor 1 11%
Other 2 22%
Readers by discipline Count As %
Computer Science 3 33%
Engineering 2 22%
Agricultural and Biological Sciences 1 11%
Mathematics 1 11%
Economics, Econometrics and Finance 1 11%
Other 1 11%