The 2nd edition is an update of the book Wavelet Theory and its Application to Pattern Recognition published in 2000. Three new chapters, which are research results conducted during 2001-2008, are added. The book consists of three parts — the first presents a brief survey of the status of pattern recognition with wavelet theory; the second contains the basic theory of wavelet analysis; the third includes applications of wavelet theory to pattern recognition. The new book provides a bibliography of 170 references including the current state-of-the-art theory and applications of wavelet analysis to pattern recognition.

This 3rd edition tackles the basic principle of deep learning as well as the application of combination of wavelet theory with deep learning to pattern recognition. Five new chapters related to the combination of wavelet theory and deep learning are added with many novel research results.The useful reference text will benefit academics, researchers, computer scientists, electronic engineers and graduate students in the field of pattern recognition, image analysis, machine learning and electrical and electronic engineering.

This is not a purely mathematical book. It presents the basic principle of wavelet theory to electrical and electronic engineers, computer scientists, and students, as well as the ideas of how wavelets can be applied to pattern recognition. It also contains many novel research results from the authors'' research team.

Basic Concepts of Document Analysis and Understanding; Basic Concepts of Fractal Dimension; Basic Concepts of Wavelet Theory; Document Analysis by Fractal Dimension; Text Extraction by Wavelet Decomposition; Rotation Invariant by Fractal Theory with Central Projection Transform (CPT); Wavelet-Based and Fractal-Based Methods for Script Identification; Writer Identification Using Hidden Markov Model in Wavelet Domain (WD-HMM).

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:

Describing non-parametric and parametric theoretic classification and the training of discriminant functions, this second edition includes new and expanded sections on neural networks, Fisher's discriminant, wavelet transform, and the method of principal components. It contains discussions on dimensionality reduction and feature selection; novel computer system architectures; proven algorithms for solutions to common roadblocks in data processing; computing models including the Hamming net, the Kohonen self-organizing map, and the Hopfield net; detailed appendices with data sets illustrating key concepts in the text; and more.

Researchers from various disciplines such as pattern recognition, statistics, and machine learning have explored the use of ensemble methodology since the late seventies. Thus, they are faced with a wide variety of methods, given the growing interest in the field. 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. The book describes in detail the classical methods, as well as the extensions and novel approaches developed recently. Along with algorithmic descriptions of each method, it also explains the circumstances in which this method is applicable and the consequences and the trade-offs incurred by using the method.

This book presents an interactive multimodal approach for efficient transcription of handwritten text images. This approach, rather than full automation, assists the expert in the recognition and transcription process.Until now, handwritten text recognition (HTR) systems are far from being perfect and heavy human intervention is often required to check and correct the results of such systems. The interactive scenario studied in this book combines the efficiency of automatic handwriting recognition systems with the accuracy of the experts, leading to a cost-effective perfect transcription of the handwritten text images.The interactive system here allows the user to repeatedly interact with the system. Hence, the quality and ergonomy of the interactive process is crucial for the success of the system. Moreover, more ergonomic multimodal interfaces are used to obtain an easier and more comfortable human-machine interaction.

This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.

This book 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. The first automatically generates database constraints from a rule-base in SpecDB. The second is a reverse engineering tool that logs the actual execution of the program from the code.