Rough-Fuzzy Pattern Recognition

• Date de parution : 20/12/2011
• Editeur : Wiley-ieee Computer Society Pr
• EAN : 9781118119716
• Série : (-)
• Support : Livre numérique

• Nombre de pages : (-)
• Collection : (-)
• Genre : (-)
• Thème : Non attribué
• Prix littéraire(s) : (-)

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processingEmphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy... Voir plus

Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processingEmphasizing 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 miningA Mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the setSelection of non-redundant and relevant features of real-valued data setsSelection of the minimum set of basis strings with maximum information for amino acid sequence analysisSegmentation of brain MR images for visualization of human tissuesNumerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This textcovering the latest findings as well as directions for future researchis recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.