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- Ten Lectures on Statistical and Structural Pattern Recognition
- Ten Lectures on Statistical and Structural Pattern Recognition
- Ten Lectures on Statistical and Structural Pattern Recognition

Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. The journal Pattern Recognition was established some 50 years ago, as the field emerged in the early years of computer science.

Published by Kluwer Academic in Dordrecht , Boston. Written in English. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern Most of the topics are accompanied by detailed algorithms and real world applications. Pattern recognition is the research area that studies the operation and design of systems that recognize patterns in this work three basic approaches of pattern recognition are analyzed: statistical pattern recognition, structural pattern recognition and neural pattern recognition. In the statistical. Explores the heart of pattern recognition concepts, methods and applications using statistical, syntactic and neural approaches. Divided into four sections, it clearly demonstrates the similarities and differences among the three approaches.

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It seems that you're in Germany. We have a dedicated site for Germany. Authors: Schlesinger , M. Preface to the English edition This monograph Ten Lectur,es on Statistical and Structural Pattern Recognition uncovers the close relationship between various well known pattern recognition problems that have so far been considered independent. The generalised problem formulations were analysed mathematically and unified algorithms were found. In addition to this bridging on the uppermost level, the book mentions several other unexpected relations within statistical and structural methods.

Preface to the English edition This monograph Ten Lectur,es on Statistical and Structural Pattern Recognition uncovers the close relationship between various well known pattern recognition problems that have so far been considered independent. The generalised problem formulations were analysed mathematically and unified algorithms were found. In addition to this bridging on the uppermost level, the book mentions several other unexpected relations within statistical and structural methods. The monograph is intended for experts, for students, as well as for those who want to enter the field of pattern recognition. The theory is built up from scratch with almost no assumptions about any prior knowledge of the reader. Even when rigorous mathematical language is used we make an effort to keep the text easy to comprehend. This approach makes the book suitable for students at the beginning of their scientific career.

This monograph explores the close relationship of various well-known pattern recognition problems that have so far been considered independent. These relationships became apparent with the discovery of formal procedures for addressing known problemsMoreThis monograph explores the close relationship of various well-known pattern recognition problems that have so far been considered independent. These relationships became apparent with the discovery of formal procedures for addressing known problems and their generalisations.

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Request PDF | Ten Lectures on Statistical and Structural Pattern Recognition | Preface. Lecture 1. Bayesian statistical decision making. Lecture.