Pattern Recognition In Bioinformatics
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Rough-Fuzzy Pattern Recognition
Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection. Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as: Soft computing in pattern recognition and data mining A mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the set Selection of non-redundant and relevant features of real-valued data sets Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis Segmentation of brain MR images for visualization of human tissues Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text—covering the latest findings as well as directions for future research—is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.
Pattern Recognition in Bioinformatics
Author: Visakan Kadirkamanathan
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-08-28
This book constitutes the refereed proceedings of the Fourth International Workshop on Pattern Recognition in Bioinformatics, PRIB 2009, held in Sheffield, UK, in September 2009. The 38 revised full papers presented were carefully reviewed and selected from numerous submissions. The topics covered by these papers range from image analysis for biomedical data to systems biology. The conference aims at crating a focus for the development and application of pattern recognition techniques in the biological domain.
Classification and Learning Using Genetic Algorithms
Author: Sanghamitra Bandyopadhyay
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-05-17
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-à-vis several widely used classifiers, including neural networks.