Book contents
- Frontmatter
- Contents
- Preface
- Notation
- 1 Introduction and Examples
- 2 Statistical Decision Theory
- 3 Linear Discriminant Analysis
- 4 Flexible Discriminants
- 5 Feed-forward Neural Networks
- 6 Non-parametric Methods
- 7 Tree-structured Classifiers
- 8 Belief Networks
- 9 Unsupervised Methods
- 10 Finding Good Pattern Features
- A Statistical Sidelines
- Glossary
- References
- Author Index
- Subject Index
1 - Introduction and Examples
Published online by Cambridge University Press: 05 August 2014
- Frontmatter
- Contents
- Preface
- Notation
- 1 Introduction and Examples
- 2 Statistical Decision Theory
- 3 Linear Discriminant Analysis
- 4 Flexible Discriminants
- 5 Feed-forward Neural Networks
- 6 Non-parametric Methods
- 7 Tree-structured Classifiers
- 8 Belief Networks
- 9 Unsupervised Methods
- 10 Finding Good Pattern Features
- A Statistical Sidelines
- Glossary
- References
- Author Index
- Subject Index
Summary
This book is primarily about pattern recognition, which covers a wide range of activities from many walks of life. It is something which we humans are particularly good at; we receive data from our senses and are often able, immediately and without conscious effort, to identify the source of the data. For example, many of us can
recognize faces we have not seen for many years, even in disguise,
recognize voices over a poor telephone line,
as babies recognize our mothers by smell,
distinguish the grapes used to make a wine, and sometimes
even recognize the vineyard and year,
identify thousands of species of flowers and
spot an approaching storm.
Science, technology and business has brought to us many similar tasks, including
diagnosing diseases,
detecting abnormal cells in cervical smears,
recognizing dangerous driving conditions,
identifying types of car, aeroplane, …,
identifying suspected criminals by fingerprints and DNA profiles,
reading Zip codes (US postal codes) on envelopes,
reading hand-written symbols (on a penpad computer),
reading maps and circuit diagrams,
classifying galaxies by shape,
picking an optimal move or strategy in a game such as chess,
identifying incoming missiles from radar or sonar signals,
detecting shoals of fish by sonar,
checking packets of frozen peas for ‘foreign bodies’,
spotting fake ‘antique’ furniture,
deciding which customers will be good credit risks and
spotting good opportunities on the financial markets.
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- Pattern Recognition and Neural Networks , pp. 1 - 16Publisher: Cambridge University PressPrint publication year: 1996
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