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This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks.

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks.

Aims to provide a treatise, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. This book brings together research articles from scientists around the world.

Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature.

This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as...

This book offers a theoretical analysis of symmetry-based clustering techniques. It includes extensive real-world applications in data mining, remote sensing imaging, MR brain imaging, gene expression data analysis, and face detection.

An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner.

This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful...

This volume contains research on how we measure poverty, inequality and welfare and how we use such measurements to devise policies to deliver social mobility. It contains ten papers, some of which were presented at the third meeting of The Theory and Empirics of Poverty, Inequality and Mobility at Queen Mary University of London, London, October 2016.
The volume begins with theoretical issues at the frontier of the literature. Three papers discuss the impact of social welfare policies on poverty measurement, and with innovations on the measurement of relative bipolarisation. Two papers...