Books in the Series In Machine Perception And Artificial Intelligence series

Keyword Spotting (KWS) has been proposed as a flexible and more error-tolerant alternative to full transcriptions. In most cases, it allows to retrieve arbitrary query words in handwritten historical document.This comprehensive compendium gives a self-contained preamble and visually attractive description to the field of graph-based KWS. The volume highlights a profound insight into each step of the whole KWS pipeline, viz. image preprocessing, graph representation and graph matching.Written by two world-renowned co-authors, this unique title combines two very current research fields of graph-based pattern recognition and document analysis. The book serves as an attractive teaching material for graduate students, as well as a useful reference text for professionals, academics and researchers.

In recent years, libraries and archives all around the world have increased their efforts to digitize historical manuscripts. To integrate the manuscripts into digital libraries, pattern recognition and machine learning methods are needed to extract and index the contents of the scanned images.The unique compendium describes the outcome of the HisDoc research project, a pioneering attempt to study the whole processing chain of layout analysis, handwriting recognition, and retrieval of historical manuscripts. This description is complemented with an overview of other related research projects, in order to convey the current state of the art in the field and outline future trends.This must-have volume is a relevant reference work for librarians, archivists and computer scientists.

A metaheuristic is a higher-level procedure designed to select a partial search algorithm that may lead to a good solution to an optimization problem, especially with incomplete or imperfect information.This unique compendium focuses on the insights of hybrid metaheuristics. It illustrates the recent researches on evolving novel hybrid metaheuristic algorithms, and prominently highlights its diverse application areas. As such, the book helps readers to grasp the essentials of hybrid metaheuristics and to address real world problems.The must-have volume serves as an inspiring read for professionals, researchers, academics and graduate students in the fields of artificial intelligence, robotics and machine learning.

The compendium presents the latest results of the most prominent competitions held in the field of Document Analysis and Text Recognition. It includes a description of the participating systems and the underlying methods on one hand and the datasets used together with evaluation metrics on the other hand. This volume also demonstrates with examples, how to organize a competition and how to make it successful. It will be an indispensable handbook to the document image analysis community.

Focuses on a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. This title aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.

Providing an overview of terrorist threats in cyberspace, this volume presents tools and technologies that can deal with these threats. It covers many topics in cyber warfare, such as terrorist use of the Internet, the Cyber Jihad, data mining tools and techniques of terrorist detection on the web, detection of terror financing, and more.

Describes opportunities for utilizing robust graph representations of data with machine learning algorithms. The authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual substance.

Software systems surround us. Software is a critical component in everything from the family car through electrical power systems to military equipment. As software plays an ever-increasing role in our lives and livelihoods, the quality of that software becomes more and more critical. However, our ability to deliver high-quality software has not kept up with those increasing demands. The economic fallout is enormous; the US economy alone is losing over US$50 billion per year due to software failures. This book presents new research into using advanced artificial intelligence techniques to guide software quality improvements. The techniques of chaos theory and data mining are brought to bear to provide new insights into the software development process. Written for researchers and practitioners in software engineering and computational intelligence, this book is a unique and important bridge between these two fields.

A guide to the fundamentals of robotics, which is set to enhance our lives in revolutionary ways. It covers practical knowledge in understanding, developing and using robots as equipment to automate a variety of industrial processes, and also examines the future possibilities for robotics.

A study of invariants. The papers are organized into two categories: foundations and applications. The foundation papers present ways of defining or analyzing invariants, and the application papers present ways in which known invariant theory is extended and applied to real-world problems.

Consists of two parts - the first contains the basic theory of wavelet analysis and the second includes applications of wavelet theory to pattern recognition. This book provides a bibliography of 170 references including the theory and applications of wavelet analysis to pattern recognition.

Data Mining is the science and technology of exploring data in order to discover previously unknown patterns. It is a part of the overall process of Knowledge Discovery from Databases (KDD). The accessibility and abundance of information today makes data mining a matter of considerable importance and necessity. This book provides an introduction to the field with an emphasis on advanced decomposition methods in general data mining tasks and for classification tasks in particular. The book presents a complete methodology for decomposing classification problems into smaller and more manageable sub-problems that are solvable by using existing tools, and then joining them together to solve the initial problem. The benefits of decomposition methodology in data mining include: increased performance (classification accuracy), conceptual simplification of the problem, enhanced feasibility with huge databases, clearer and more comprehensible results, reduced runtime by solving smaller problems and by using parallel/distributed computation, and the opportunity of using different techniques for individual sub-problems.

A study of texture analysis in machine vision. It contains extended and revised versions of papers presented at a workshop held at the University of Oulu, Finland, in 1999. The first part of the book deals with texture analysis methodology, while the second covers various applications.

Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. Dedicated to the field of decision trees in data mining, this book covers various aspects of this technique.

Provides a forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). This title accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning.

Introduces SpecDB, an intelligent database created to represent and host software specifications in a machine-readable format, based on the principles of artificial intelligence and unit testing database operations. SpecDB is demonstrated via two automated intelligent tools.

Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. This book aims to impose a degree of order upon this diversity by presenting a coherent and unified repository of ensemble methods, theories, trends, challenges and applications.

Suitable for researchers, technicians and graduate students, this book includes scientific papers that show a broad spectrum of actual research topics and techniques used to solve challenging problems in the areas of computer vision and image analysis.

Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Scales well to big data Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many open source data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection

Provides an approach to pattern recognition in which objects are characterized by relations to other objects instead of by using features or models. This book is intended for researchers and systems designers studying or developing pattern recognition systems.