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Advantages and pitfalls of pattern r...

Falsaperla, Susanna.

Advantages and pitfalls of pattern recognitionselected cases in geophysics /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advantages and pitfalls of pattern recognitionHorst Langer, Susanna Falsaperla, Conny Hammer.
其他題名:	selected cases in geophysics /
作者:	Langer, Horst.
其他作者:	Falsaperla, Susanna.
出版者:	San Diego :Elsevier,2020.
面頁冊數:	1 online resource (352 p.)
附註:	4.5.2 Integrated inversion of geophysical data
標題:	GeophysicsData processing.
電子資源:	https://www.sciencedirect.com/science/book/9780128118429
ISBN:	9780128118436

Advantages and pitfalls of pattern recognitionselected cases in geophysics /
Langer, Horst.

Advantages and pitfalls of pattern recognitionselected cases in geophysics /[electronic resource] :Horst Langer, Susanna Falsaperla, Conny Hammer. - San Diego :Elsevier,2020. - 1 online resource (352 p.) - Computational geophysics series ;v. 3. - Computational geophysics series ;v. 3..

4.5.2 Integrated inversion of geophysical data

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

ISBN: 9780128118436Subjects--Topical Terms:

281286
Geophysics
--Data processing.Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: QC808.6

Dewey Class. No.: 551.0285

Advantages and pitfalls of pattern recognitionselected cases in geophysics /
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505 0 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 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)
505 8 $a 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
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