Advantages and Pitfalls of Pattern Recognition presents various methods of pattern recognition and classification, useful to geophysicists, geochemists, geologists, geographers, data analysts, and educators and students of geosciences. Scientific and technological progress has dramatically improved the knowledge of our planet with huge amounts of digital data available in various fields of Earth Sciences, such as geology, geophysics, and geography. This has led to a new perspective of data analysis, requiring specific techniques that take several features into consideration rather than single parameters. Pattern recognition techniques offer a suitable key for processing and extracting useful information from the data of multivariate analysis. This book explores both supervised and unsupervised pattern recognition techniques, while providing insight into their application.
1. 2.1. Introduction - p. 33 2. 2.2. Discriminant analysis - p. 35 3. 2.3. The linear perceptron - p. 40 4. 2.4. Solving the XOR problem: classification using multilayer perceptrons (MLPs) - p. 46 5. 2.5. Support vector machines (SVMs) - p. 53 6. 2.6. Hidden Markov Models (HMMs)/sequential data - p. 59 7. 2.7. Bayesian networks - p. 69 8. Appendix 2 - p. 73 9. Chapter 3. Unsupervised learning - p. 87 10. Part II. Example applications - p. 124 11. Chapter 4. Applications of supervised learning - p. 127 12. Chapter 5. Applications with unsupervised learning - p. 189 13. Part III. A posteriori analysis - p. 234 14. Chapter 6. A posteriori analyses—advantages and pitfalls of pattern recognition techniques - p. 237 15. Chapter 7. Software manuals - p. 261 16. References - p. 315 17. Index - p. 327
Advantages and Pitfalls of Pattern Recognition presents various methods of pattern recognition and classification, useful to geophysicists, geochemists, geologists, geographers, data analysts, and educators and students of geosciences. Scientific and technological progress has dramatically improved the knowledge of our planet with huge amounts of digital data available in various fields of Earth Sciences, such as geology, geophysics, and geography. This has led to a new perspective of data analysis, requiring specific techniques that take several features into consideration rather than single parameters. Pattern recognition techniques offer a suitable key for processing and extracting useful information from the data of multivariate analysis. This book explores both supervised and unsupervised pattern recognition techniques, while providing insight into their application.
1. 2.1. Introduction - p. 33 2. 2.2. Discriminant analysis - p. 35 3. 2.3. The linear perceptron - p. 40 4. 2.4. Solving the XOR problem: classification using multilayer perceptrons (MLPs) - p. 46 5. 2.5. Support vector machines (SVMs) - p. 53 6. 2.6. Hidden Markov Models (HMMs)/sequential data - p. 59 7. 2.7. Bayesian networks - p. 69 8. Appendix 2 - p. 73 9. Chapter 3. Unsupervised learning - p. 87 10. Part II. Example applications - p. 124 11. Chapter 4. Applications of supervised learning - p. 127 12. Chapter 5. Applications with unsupervised learning - p. 189 13. Part III. A posteriori analysis - p. 234 14. Chapter 6. A posteriori analyses—advantages and pitfalls of pattern recognition techniques - p. 237 15. Chapter 7. Software manuals - p. 261 16. References - p. 315 17. Index - p. 327
