Computational Intelligence Paradigms: Innovative Applications (Studies in Computational Intelligence, 137)

✍ Scribed by Mika Sato-Ilic (editor), Maria Virvou (editor), George A Tsihrintzis (editor), Valentina Emilia Balas (editor), Canicious Abeynayake (editor)

• Publisher: Springer
• Year: 2008
• Tongue: English
• Leaves: 281
• Category: Library

✦ Synopsis

System designers are faced with a large set of data which has to be analysed and processed efficiently. Advanced computational intelligence paradigms present tremendous advantages by offering capabilities such as learning, generalisation and robustness. These capabilities help in designing complex systems which are intelligent and robust.

The book includes a sample of research on the innovative applications of advanced computational intelligence paradigms. The characteristics of computational intelligence paradigms such as learning, generalization based on learned knowledge, knowledge extraction from imprecise and incomplete data are the extremely important for the implementation of intelligent machines. The chapters include architectures of computational intelligence paradigms, knowledge discovery, pattern classification, clusters, support vector machines and gene linkage analysis. We believe that the research on computational intelligence will simulate great interest among designers and researchers of complex systems. It is important to use the fusion of various constituents of computational intelligence to offset the demerits of one paradigm by the merits of another.

✦ Table of Contents

Title Page
Preface
Contents
An Introduction to Computational Intelligence Paradigms
Introduction
Computational Intelligence Models
Neural Computational Models
Fuzzy Computational Models
Evolutionary Computational Models
Hybrid Computational Models
Chapters Included in This Book
Summary
Resources
Journals
Special Issue of Journals
Conferences
Conference Proceedings
Book Series
Books
Book Chapters
References
A Quest for Adaptable and Interpretable Architectures of Computational Intelligence
Introductory Comments
Generic Logic Processing Realized with the Aid of Logic Neurons
Triangular Norms: t- Norms
Triangular Norms: t-Conorms
Uninorms – A Hybrid Structure of Logic Operators
Logic Neurons
Aggregative Neurons: OR and AND Neurons
Referential (Reference) Neurons
Uninorm-Based Logic Neurons
Architectures of Logic Networks
Logic Processor in the Processing of Fuzzy Logic Functions: A Canonical Realization
Heterogeneous Logic Networks
Unineuron-Based Topologies of Logic Networks
Learning of Unineurons and Networks of Unineurons
Linguistic Modeling of Relationships between Information Granules: The Paradigm of Granular (Fuzzy) Models
Conclusions
References
Membership Map: A Data Transformation for Knowledge Discovery Based on Granulation and Fuzzy Membership Aggregation
Introduction
Background
Related Work
Data Preprocessing
Data Partitioning and Labeling
MembershipMap Generation
Properties of the Crisp, Fuzzy, and Possibilistic Maps
The Crisp MembershipMap
The Fuzzy MembershipMap
The Possibilistic MembershipMap
Exploring the Membership Maps
Identifying Seed Points
Identifying Noise Points and Outliers
Identifying Boundary Points
Illustrative Example
Discussion
Data Labeling for Semi-Supervised Learning
Experimental Results
Data Sets
Membership Generation
Identifying Regions of Interest
Clustering the Membership Maps
Classification Using the Membership Maps
Application: Color Image Segmentation
Conclusions
References
Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern Classification
Introduction
Fuzzy ARTMAP Principles and Dynamics
Advanced FAM-Based Developments
Modifications to FAM
FAM-Based New Algorithms
Advanced FAM-Based Applications
Experimental Evaluation of FAM-Based Algorithms
Discussion
References
Large Margin Methods for Structured Output Prediction
Introduction
Discriminative Models for Structured Output Learning
Structured Output Problems
Learning with Structured Outputs
Sequence Labeling: From HMMs to Chain CRFs
Large Margin Approaches for Structured Output Learning
Multiclass SVMs
Maximizing the Margin in Structured Output
Marginal Variables Method
Iterative Method
Min-Max Method
Generalization Bounds
Experimental Results
Sequence Labeling Learning
Sequence Alignment Learning
Sequence Parse Learning
Discussion and Conclusions
References
Ensemble MLP Classifier Design
Introduction
MLP Classifiers and Ensembles
Diversity/Accuracy and MCS
Error Correcting Output Coding (ECOC) and Multi-class Problems
Examples
Benchmark Data
Face Data
Discussion
Conclusion
References
Functional Principal Points and Functional Cluster Analysis
Introduction
Preliminaries
Definition of Principal Points
Definition of Random Functions
Definition of Functional Principal Points of Random Functions
$K$-Means Functional Clustering
Orthonormal Basis Transformation of Functions
Some Examples of Functional Principal Points
The Case That 2-Dimensional Coefficient Vector of Linear Random Function Following Bivariate Normal Distribution
The Case That p-Dimensional Coefficient Vector of Polynomial Random Function Following p-Variate Normal Distribution
The Case That Fourier Polynomial Random Function Following Multivariate Normal Distribution
Optimal Functional Clustering and Functional Principal Points
The Numbers of Local Solutions in Functional k-Means Clustering
Summary
References
Clustering with Size Constraints
Introduction
The FCM Algorithm
Equi-sized Clusters
Examples for Uniform Clustering
Limiting the Size of Single Clusters Only
A Distance Measure for Sets of Prototypes
Conclusions
References
Cluster Validating Techniques in the Presence of Duplicates
Introduction
Outline of Our Approach
Clustering Algorithms
Partitioning Around Medoids (PAM)
Validation Techniques
Silhouette Index
Calinski and Harabasz Index
Baker and Hubert Index
Experimental Evaluation
Coefficient Relibility wrt Record Based Duplicates
Coefficient Reliability wrt Value Based Duplicates
Conclusion
Fuzzy Blocking Regression Models
Introduction
Fuzzy Clustering
Variable Based Fuzzy Clustering
Variable Based Dissimilarity
Variable Based Fuzzy Clustering Methods
Blocking Regression Model
Fuzzy Blocking Regression Model
Fuzzy Blocking Regression Model Using Fuzzy Intercepts
Variable Based Fuzzy Blocking Regression Model
Numerical Examples
Conclusion
References
Support Vector Machines and Features for Environment Perception in Mobile Robotics
Introduction
Support Vector Machines
VC Dimension
Multiclassification with SVM
Practical Considerations
Features
Features in Lidar Space
Features in Camera Space: A Brief Survey
Applications
Learning to Label Environment Places
Recognizing Objects in Images
Conclusion
References
Linkage Analysis in Genetic Algorithms
Introduction
Linkage Identification in Genetic Algorithms
Problem Decomposability
Linkage Identification by Perturbation
D$^5$: Dependency Detection by Fitness Clustering
Context Dependent Crossover
Conclusion
References
Author Index