Pattern Recognition and Classification

The concept of pattern is universal in intelligence. This book recounts recent progress in pattern recognition using neural networks and functional networks, including wavelet transforms in the contex

Computer Manual (MATHEMATICA version) to accompany Pattern Classification, Third Edition, and its associated software contains all the MATHEMATICA code for the dynamic figures in the electronic versio

This book presents recent developments and research trends in the field of feature selection for data and pattern recognition, highlighting a number of latest advances.The field of feature selection i

Ever wondered what the state of the art is in machine learning and data mining? Well, now you can find out. This book constitutes the refereed proceedings of the 5th International Conference on Machin

This book constitutes the refereed proceedings of the First International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2016, held in Bidar, Karnataka, India, in Dece

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

The book is a collection of invited chapters by renowned experts and is part of a series on Language Processing, Pattern Recognition, and Intelligent Systems. The content is wide-ranging, encompassing

This text reviews the field of digital image processing from the different perspectives offered by the separate domains of signal processing and pattern recognition. The book describes a rich array of

Similarity and analogy are fundamental in human cognition. They are crucial for recognition and classification, and have been associated with scientific discovery and creativity. Successful learning is generally less dependent on the memorization of isolated facts and abstract rules than it is on the ability to identify relevant bodies of knowledge already stored as the starting point for new learning. Similarity and analogy play an important role in this process - a role that in recent years has received much attention from cognitive scientists. Any adequate understanding of similarity and analogy requires the integration of theory and data from diverse domains. This interdisciplinary volume explores current developments in research and theory from psychological, computational, and educational perspectives, and considers their implications for learning and instruction. Well-known cognitive scientists examine the psychological processes involved in reasoning by similarity and analogy,

Recent decades have witnessed an explosion in neuroscientific and related research treating aesthetic response. This book integrates this research with insights from philosophical aesthetics to propose new answers to longstanding questions about beauty and sublimity. Hogan begins by distinguishing what we respond to as beautiful from what we count socially as beautiful. He goes on to examine the former in terms of information processing (specifically, prototype approximation and non-habitual pattern recognition) and emotional involvement (especially of the endogenous reward and attachment systems). In the course of the book, Hogan examines such issues as how universal principles of aesthetic response may be reconciled with individual idiosyncrasy, how it is possible to argue rationally over aesthetic response, and what role personal beauty and sublimity might play in the definition of art. To treat these issues, the book considers works by Woolf, Wharton, Shakespeare, Arthur Miller, Be

Recent decades have witnessed an explosion in neuroscientific and related research treating aesthetic response. This book integrates this research with insights from philosophical aesthetics to propose new answers to longstanding questions about beauty and sublimity. Hogan begins by distinguishing what we respond to as beautiful from what we count socially as beautiful. He goes on to examine the former in terms of information processing (specifically, prototype approximation and non-habitual pattern recognition) and emotional involvement (especially of the endogenous reward and attachment systems). In the course of the book, Hogan examines such issues as how universal principles of aesthetic response may be reconciled with individual idiosyncrasy, how it is possible to argue rationally over aesthetic response, and what role personal beauty and sublimity might play in the definition of art. To treat these issues, the book considers works by Woolf, Wharton, Shakespeare, Arthur Miller, Be

Engel (English, U. of the South, Tennessee) explores pattern recognition and how the classical Art of Memory--by way of 17th-century aesthetic principles--gained a foothold in the work of 19th-century

The problem of inducing, learning or inferring grammars has been studied for decades, but only in recent years has grammatical inference emerged as an independent field with connections to many scientific disciplines, including bio-informatics, computational linguistics and pattern recognition. This book meets the need for a comprehensive and unified summary of the basic techniques and results, suitable for researchers working in these various areas. In Part I, the objects of use for grammatical inference are studied in detail: strings and their topology, automata and grammars, whether probabilistic or not. Part II carefully explores the main questions in the field: What does learning mean? How can we associate complexity theory with learning? In Part III the author describes a number of techniques and algorithms that allow us to learn from text, from an informant, or through interaction with the environment. These concern automata, grammars, rewriting systems, pattern languages or tra

Now available in the Cambridge Mathematical Library, the classic work from Luis Santaló. Integral geometry originated with problems on geometrical probability and convex bodies. Its later developments, however, have proved to be useful in several fields ranging from pure mathematics (measure theory, continuous groups) to technical and applied disciplines (pattern recognition, stereology). The book is a systematic exposition of the theory and a compilation of the main results in the field. The volume can be used to complement courses on differential geometry, Lie groups or probability or differential geometry. It is ideal both as a reference and for those wishing to enter the field.

This book is devoted to some mathematical methods that arise in two domains of artificial intelligence: neural networks and qualitative physics. Professor Aubin makes use of control and viability theory in neural networks and cognitive systems, regarded as dynamical systems controlled by synaptic matrices, and set-valued analysis that plays a natural and crucial role in qualitative analysis and simulation. This allows many examples of neural networks to be presented in a unified way. In addition, several results on the control of linear and nonlinear systems are used to obtain a 'learning algorithm' of pattern classification problems, such as the back-propagation formula, as well as learning algorithms of feedback regulation laws of solutions to control systems subject to state constraints. This book will be of value to anyone with an interest in neural networks and cognitive systems.

This second edition volume expands on the first edition with new developments on Toll-Like Receptors (TLRs) controlling events such as cross-priming of associated pattern recognition receptors, post-t

Elliott Wave analysis provides a comprehensive method of technical analysis based on crowd psychology and pattern recognition. The challenge most traders confront is translating Elliott Wave into trad

The book presents findings, views and ideas on what exact problems of image processing, pattern recognition and generation can be efficiently solved by cellular automata architectures. This volume pro

The book is a collection of invited chapters by experts in Chinese document and text processing, and is part of a series on Language Processing, Pattern Recognition, and Intelligent Systems. The chapt

This book is about visualization, systematically incorporating ourfantastic pattern-recognition into the problem-solving process, andfocusing on parallel coordinates. The barrier, imposed

Topics and FeaturesProvides a thorough discussion of several tasks for image processing, pattern recognition, computer vision, robotics and computer graphics using the geometric algebra frameworkIntr