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基本説明
New in paperback. Hardcover was published in 1995. Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks.
Full Description
This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author's website: www.stats.ox.ac.uk/‾ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.
Contents
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; Appendix: statistical sidelines; Glossary; References; Author index; Subject index.
