The support vector machine (SVM) is a supervised learning method that generates input-output mapping functions from a set of labeled training data. The mapping function can be either a classification function, i.e., the category of the input data, or a regression function. For classification, nonlinear kernel functions are often used to...

Why a book on fault-tolerant search algorithms? Searching is one of the fundamental problems in computer science. Time and again algorithmic and combinatorial issues originally studied in the context of search find application in the most diverse areas of computer science and discrete mathematics. On the other hand, fault-tolerance is a...

Text Mining: Applications and Theory presents the state-of-the-art algorithms for text mining from both the academic and industrial perspectives.  The contributors span several countries and scientific domains: universities, industrial corporations, and government laboratories, and demonstrate the use of techniques from machine...

Books on bioinformatics which began appearing in the mid 80s primarily served gene-hunters, and biologists who wished to construct family trees showing tidy lines of descent. Given the great pharmaceutical industry interest in genes, this trend has continued in most subsequent texts. These deal extensively with the exciting topic of gene...

Visual Basic and Visual Basic .NET for Scientists and Engineers begins with an overview of the Visual Basic and Visual Basic .NET IDEs, their important characteristics, and how the development environments can be manipulated to suit developers' needs. After a solid discussion of VB and VB .NET forms, controls, and namespaces,...

The second edition of Introduction to Data Compression builds on the features that made the first the logical choice-for practitioners who need a comprehensive guide to compression for all types of multimedia and instructors who want to equip their students with solid foundations in these increasingly important and diverse...

Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for...

This book suits both graduate students and researchers with a focus on discovering knowledge from scientific data. The use of computational power for data analysis and knowledge discovery in scientific disciplines has found its roots with the revolution of high-performance computing systems. Computational science in physics,...

The over 450 papers presented were carefully reviewed and selected from numerous...