Ulteriori informazioni
This book provides readers with the state-of-the-art algorithms in the area of pattern recognition as well as a presentation of the cutting edge applications within the field. The book introduces recent achievements in the areas of theoretical pattern recognition including statistical and Bayesian pattern recognition, structural pattern recognition, neural networks, classification and data mining, evolutionary approaches to optimisation, and knowledge based systems amongst other areas.
The collection of articles presented is from the 2007 Workshop on Advances in Pattern Recognition, organised in conjunction with the 5th International Summer School on Pattern Recognition.
An invaluable reference for researchers, academics and postgraduate students in the area of pattern recognition, this book will provide insights and will support practitioners concerned with the state-of-the-art technology in this area.
Info autore
Professor Sameer Singh is Director of the Research School of Informatics, Loughborough University, UK, and serves as Editor-in-Chief of the Springer journal, Pattern Analysis and Applications
Riassunto
Overview andGoals Pattern recognition has evolved as a mature field of data analysis and its practice involves decision making using a wide variety of machine learning tools. Over the last three decades, substantial advances have been made in the areas of classification, prediction, optimisation and planning algorithms. Inparticular, the advances made in the areas of non-linear classification, statistical pattern recognition, multi-objective optimisation, string matching and uncertainty management are notable. These advances have been triggered by the availability of cheap computing power which allows large quantities of data to be processed in a very short period of time, and therefore developed algorithms can be tested easily on real problems. The current focus of pattern recognition research and development is to take laboratory solutions to commercial applications. The main goal of this book is to provide researchers with some of the latest novel techniques in the area of pattern recognition, and to show the potential of such techniques on real problems. The book will provide an excellent background to pattern recognition students and researchers into latest algorithms for pattern matching, and classification and their practical applications for imaging and non-imaging applications. Organization and Features The book is organised in two parts. The first nine chapters of the book describe novel advances in the areas of graph matching, information fusion, data clustering and classification, feature extraction and decision making under uncertainty.
Testo aggiuntivo
From the reviews:
“The book is divided into two parts. … Although in my opinion many articles could have presented more proper conclusions or deeper proofs and evidences, and some of them focused on the practicability of machine learning and pattern recognition from a theoretically point of view, the scientific relevance of the content of the book is good. The authors presented their work at the International Workshop on Advances in Pattern Recognition 2007. Accordingly, the target audience is also academic.” (Eleazar Jimenez Serrano, IAPR Newsletter, Vol. 32 (4), October, 2010)
Relazione
From the reviews:
"The book is divided into two parts. ... Although in my opinion many articles could have presented more proper conclusions or deeper proofs and evidences, and some of them focused on the practicability of machine learning and pattern recognition from a theoretically point of view, the scientific relevance of the content of the book is good. The authors presented their work at the International Workshop on Advances in Pattern Recognition 2007. Accordingly, the target audience is also academic." (Eleazar Jimenez Serrano, IAPR Newsletter, Vol. 32 (4), October, 2010)
