Machine learning methods in the environmental sciences: neural networks and kernels
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge [u.a.]
Cambridge Univ. Press
2009
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIII, 349 S. graph. Darst. |
| ISBN: | 9780521791922 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV035806742 | ||
| 003 | DE-604 | ||
| 005 | 20100715 | ||
| 007 | t | ||
| 008 | 091104s2009 d||| |||| 00||| eng d | ||
| 020 | |a 9780521791922 |c hbk |9 978-0-521-79192-2 | ||
| 035 | |a (OCoLC)320802276 | ||
| 035 | |a (DE-599)GBV608074268 | ||
| 040 | |a DE-604 |b ger | ||
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| 100 | 1 | |a Hsieh, William Wei |d 1955- |e Verfasser |0 (DE-588)139904557 |4 aut | |
| 245 | 1 | 0 | |a Machine learning methods in the environmental sciences |b neural networks and kernels |c William W. Hsieh |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2009 | |
| 300 | |a XIII, 349 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Environmental sciences | |
| 650 | 4 | |a Machine learning | |
| 650 | 0 | 7 | |a Methode |0 (DE-588)4038971-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Umweltwissenschaften |0 (DE-588)4137364-9 |2 gnd |9 rswk-swf |
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| 689 | 0 | |5 DE-604 | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018665755&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-018665755 | ||
Datensatz im Suchindex
| _version_ | 1804140755442204672 |
|---|---|
| adam_text | Contents
Preface
page
ix
List of abbreviations
xii
1
Basic notions in classical data analysis
1
1.1
Expectation and mean
1
1.2
Variance and covariance
2
1.3
Correlation
3
1.4
Regression
7
1.5
Bayes
theorem
12
1.6
Discriminant functions and classification
14
1.7
Clustering
16
Exercises
18
2
Linear multivariate statistical analysis
20
2.1
Principal component analysis (PCA)
20
2.2
Rotated PCA
40
2.3
PCA for vectors
48
2.4
Canonical correlation analysis (CCA)
49
Exercises
57
3
Basic time series analysis
58
3.1
Spectrum
58
3.2
Windows
65
3.3
Filters
66
3.4
Singular spectrum analysis
68
3.5
Multichannel singular spectrum analysis
74
3.6
Principal oscillation patterns
75
3.7
Spectral principal component analysis
82
Exercises
85
4
Feed-forward neural network models
86
4.1
McCulloch and Pitts model
87
vi
Contents
4.2
Perceptrons
87
4.3
Multi-layer perceptrons
(MLP)
92
4.4
В
ack-propagation
97
4.5
Hidden neurons
102
4.6
Radial basis functions (RBF)
105
4.7
Conditional probability distributions
108
Exercises
112
Nonlinear optimization
113
5.1
Gradient descent method
115
5.2
Conjugate gradient method
116
5.3
Quasi-Newton
methods
120
5.4
Nonlinear least squares methods
121
5.5
Evolutionary computation and genetic algorithms
124
Exercises
126
Learning and generalization
127
6.1
Mean squared error and maximum likelihood
127
6.2
Objective functions and robustness
129
6.3
Variance and bias errors
133
6.4
Reserving data for validation
134
6.5
Regularization
135
6.6
Cross-validation
136
6.7
Bayesian neural networks (BNN)
138
6.8
Ensemble of models
145
6.9
Approaches to predictive uncertainty
150
6.10
Linearization from time-averaging
151
Exercises
155
Kernel methods
157
7.1
From neural networks to kernel methods
157
7.2
Primal and dual solutions for linear regression
159
7.3
Kernels
161
7.4
Kernel ridge regression
164
7.5
Advantages and disadvantages
165
7.6
The pre-image problem
167
Exercises
169
Nonlinear classification
170
8.1
Multi-layer perceptron classifier
171
8.2
Multi-class classification
175
8.3
Bayesian neural network (BNN) classifier
176
8.4
Support vector machine (SVM) classifier
177
8.5
Forecast verification
187
Contents
vii
8.6 Unsupervised
competitive
learning
193
Exercises
195
9
Nonlinear regression
196
9.1
Support vector regression (SVR)
196
9.2
Classification and regression trees (CART)
202
9.3
Gaussian processes (GP)
206
9.4
Probabilistic forecast scores
211
Exercises
212
10
Nonlinear principal component analysis
213
10.1
Auto-associative
NN
for nonlinear PCA
214
10.2
Principal curves
231
10.3
Self-organizing maps
(SOM)
233
10.4
Kernel principal component analysis
237
10.5
Nonlinear complex PCA
240
10.6
Nonlinear singular spectrum analysis
244
Exercises
251
11
Nonlinear canonical correlation analysis
252
11.1
MLP-based NLCCA model
252
11.2
Robust NLCCA
264
Exercises
273
12
Applications in environmental sciences
274
12.1
Remote sensing
275
12.2
Oceanography
286
12.3
Atmospheric science
292
12.4
Hydrology
312
12.5
Ecology
314
Exercises
317
Appendices
A Sources for data and codes
318
В
Lagrange
multipliers
319
References
322
Index
345
|
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| discipline | Informatik |
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| id | DE-604.BV035806742 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:05:01Z |
| institution | BVB |
| isbn | 9780521791922 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018665755 |
| oclc_num | 320802276 |
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| owner | DE-473 DE-BY-UBG DE-703 |
| owner_facet | DE-473 DE-BY-UBG DE-703 |
| physical | XIII, 349 S. graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Cambridge Univ. Press |
| record_format | marc |
| spelling | Hsieh, William Wei 1955- Verfasser (DE-588)139904557 aut Machine learning methods in the environmental sciences neural networks and kernels William W. Hsieh 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2009 XIII, 349 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Environmental sciences Machine learning Methode (DE-588)4038971-6 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Umweltwissenschaften (DE-588)4137364-9 gnd rswk-swf Umweltwissenschaften (DE-588)4137364-9 s Maschinelles Lernen (DE-588)4193754-5 s Methode (DE-588)4038971-6 s DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018665755&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hsieh, William Wei 1955- Machine learning methods in the environmental sciences neural networks and kernels Environmental sciences Machine learning Methode (DE-588)4038971-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Umweltwissenschaften (DE-588)4137364-9 gnd |
| subject_GND | (DE-588)4038971-6 (DE-588)4193754-5 (DE-588)4137364-9 |
| title | Machine learning methods in the environmental sciences neural networks and kernels |
| title_auth | Machine learning methods in the environmental sciences neural networks and kernels |
| title_exact_search | Machine learning methods in the environmental sciences neural networks and kernels |
| title_full | Machine learning methods in the environmental sciences neural networks and kernels William W. Hsieh |
| title_fullStr | Machine learning methods in the environmental sciences neural networks and kernels William W. Hsieh |
| title_full_unstemmed | Machine learning methods in the environmental sciences neural networks and kernels William W. Hsieh |
| title_short | Machine learning methods in the environmental sciences |
| title_sort | machine learning methods in the environmental sciences neural networks and kernels |
| title_sub | neural networks and kernels |
| topic | Environmental sciences Machine learning Methode (DE-588)4038971-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Umweltwissenschaften (DE-588)4137364-9 gnd |
| topic_facet | Environmental sciences Machine learning Methode Maschinelles Lernen Umweltwissenschaften |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018665755&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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