Now in Paperback
Now in paperback: the most 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.
* The most reliable account of the subject available * now in paperback
* Unparalleled coverage with valuable insights into the theory and a wide
range of applications * Real case-studies, data sets and examples help
build skills and understanding
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.
Series: London Mathematical Society Lecture Note Series (No. 347)
Paperback (ISBN-13: 9780521705646)
Young scientists in Russia are continuing the outstanding tradition of Russian mathematics in their home country, in spite of the post-Soviet diaspora. This collection, the second of two, showcases the recent achievements of young Russian mathematicians and the strong research groups they are associated with. The first collection focused on geometry and number theory; this one concentrates on combinatorial and algebraic geometry and topology. The articles are mainly surveys of the recent work of the research groups and contain a substantial number of new results. Topics covered include algebraic geometry over Lie groups, cohomological aspects of toric topology, the Borsuk partition problem, and embedding and knotting of manifolds in Euclidean spaces. The authors are A. E. Guterman, I. V. Kazachkov, A. V. Malyutin, D. V. Osipov, T. E. Panov, A. M. Raigorodskii, A. B. Skopenkov and V. V. Ten.
* Reflects current expertise in the Russian schools of mathematics, on
a wide range of topics from classical problems to up-to-the minute research
* Articles are based on lecture courses given at British universities;
all contain extensive bibliographies * Ideal for experienced researchers
wanting a quick overview; also, researchers interested in the particular
strengths of the Russian schools of mathematics
Contents
Preface; 1. Rank and determinant functions for matrices over semi-rings A. E. Guterman; 2. Algebraic geometry over Lie algebras I. V. Kazachkov; 3. Destabilization of closed braids A. V. Malyutin; 4. n-dimensional local fields and adeles on n-dimensional schemes D. V. Osipov; 5. Cohomology of face rings, and torus actions T. E. Panov; 6. Three lectures on the Borsuk partition problem A. M. Raigorodskii; 7. Embedding and knotting of manifolds in Euclidean spaces A. B. Skopenkov; 8. On Maxwellian and Boltzmann distributions V. V. Ten.