| Description |
1 online resource (352 pages) |
| Series |
Computational geophysics series ; v. 3 |
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Computational geophysics series ; v. 3
|
| Contents |
Front Cover; Advantages and Pitfalls of Pattern Recognition; Advantages and Pitfalls of Pattern Recognition; Copyright; Contents; Preface; Acknowledgments; I -- From data to methods; 1 -- Patterns, objects, and features; 1.1 Objects and patterns; 1.2 Features; 1.2.1 Types; 1.2.2 Feature vectors; 1.2.3 Feature extraction; 1.2.3.1 Delineating segments; 1.2.3.2 Delineating regions; 1.2.4 Transformations; 1.2.4.1 Karhunen-Loève transformation (Principal Component Analysis); 1.2.4.2 Independent Component Analysis; 1.2.4.3 Fourier transform; 1.2.4.4 Short-time Fourier transform and spectrograms |
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1.2.4.5 Discrete wavelet transforms1.2.5 Standardization, normalization, and other preprocessing steps; 1.2.5.1 Comments; 1.2.5.2 Outlier removal; 1.2.5.3 Missing data; 1.2.6 Curse of dimensionality; 1.2.7 Feature selection; Appendix 1 Basic notions on statistics; A1.1 Statistical parameters of an ensemble; A1.2 Distinction of ensembles; 2 -- Supervised learning; 2.1 Introduction; 2.2 Discriminant analysis; 2.2.1 Test ban treaty-some history; 2.2.2 The MS-mb criterion for nuclear test identification; 2.2.3 Linear Discriminant Analysis; 2.3 The linear perceptron |
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2.4 Solving the XOR problem: classification using multilayer perceptrons (MLPs)2.4.1 Nonlinear perceptrons; 2.5 Support vector machines (SVMs); 2.5.1 Linear SVM; 2.5.2 Nonlinear SVM, kernels; 2.6 Hidden Markov Models (HMMs)/sequential data; 2.6.1 Background-from patterns and classes to sequences and processes; 2.6.2 The three problems of HMMs; 2.6.3 Including prior knowledge/model dimensions and topology; 2.6.4 Extension to conditional random fields; 2.7 Bayesian networks; Appendix 2; Appendix 2.1 Fisher's linear discriminant analysis; Appendix 2.2 The perceptron; Backpropagation |
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Appendix 2.3 SVM optimization of the marginsAppendix 2.4. Hidden Markov models; Appendix 2.4.1. Evaluation; Appendix 2.4.2. Decoding-the Viterbi algorithm; Appendix 2.4.3. Training-the expectation-maximization /Baum-Welch algorithm; 3 -- Unsupervised learning; 3.1 Introduction; 3.1.1 Metrics of (dis)similarity; 3.1.2 Clustering; 3.1.2.1 Partitioning clustering; 3.1.2.1.1 Fuzzy clustering; 3.1.2.2 Hierarchical clustering; 3.1.2.3 Density-based clustering; 3.2 Self-Organizing Maps; 3.2.1 Training of an SOM; Appendix 3; Appendix 3.1. Analysis of variance (ANOVA) |
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Appendix 3.2 Minimum distance property for the determinant criterionAppendix 3.3. SOM quality; Topological error; Designing the map; II -- Example applications; 4 -- Applications of supervised learning; 4.1 Introduction; 4.2 Classification of seismic waveforms recorded on volcanoes; 4.2.1 Signal classification of explosion quakes at Stromboli; 4.2.2 Cross-validation issues; 4.3 Infrasound classification; 4.3.1 Infrasound monitoring at Mt Etna-classification with SVM; 4.4 SVM classification of rocks; 4.5 Inversion with MLP; 4.5.1 Identification of parameters governing seismic waveforms |
| Notes |
4.5.2 Integrated inversion of geophysical data |
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Print version record |
| Subject |
Geophysics -- Data processing.
|
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Pattern perception.
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Geophysics -- Data processing
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Pattern perception
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| Genre/Form |
Electronic books
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| Form |
Electronic book
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| Author |
Falsaperla, Susanna.
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Hammer, Conny.
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| ISBN |
9780128118436 |
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0128118431 |
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