Machine learning: the art and science of algorithms that make sense of data
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge Univ. Press
2012
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | XVII, 396 S. Ill., graph. Darst. |
| ISBN: | 9781107422223 9781107096394 |
Internformat
MARC
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| 020 | |a 9781107096394 |c hbk |9 978-1-107-09639-4 | ||
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| 100 | 1 | |a Flach, Peter |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Machine learning |b the art and science of algorithms that make sense of data |c Peter Flach |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge |b Cambridge Univ. Press |c 2012 | |
| 300 | |a XVII, 396 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-024974976 | ||
Datensatz im Suchindex
| _version_ | 1804149077392228352 |
|---|---|
| adam_text | Contents
Preface
xv
Prologue:
A machine learning sampler
1
1
The ingredients of machine learning
13
1.1
Tasks: the problems that can be solved with machine learning
....... 14
Looking for structure
............................... 16
Evaluating performance on a task
........................ 18
1.2
Models: the output of machine learning
.................... 20
Geometric models
................................. 21
Probabilistic models
................................ 25
Logical models
................................... 32
Grouping and grading
............................... 36
1.3
Features: the workhorses of machine learning
................ 38
Two uses of features
................................ 40
Feature construction and transformation
................... 41
Interaction between features
.......................... 44
1.4
Summary and outlook
.............................. 48
What you ll
ind
in the rest of the book
..................... 48
2
Binary classification and related tasks
49
2.1
Classification
....................................52
ix
x
Contents
Assessing
classification
performance
......................53
Visualising classification performance
.....................58
2.2
Scoring and ranking
................................61
Assessing and visualising ranking performance
................63
Turning rankers into classifiers
.........................69
2.3
Class probability estimation
...........................72
Assessing class probability estimates
......................73
Turning rankers into class probability estimators
...............76
2.4
Binary classification and related tasks: Summary and further reading
. . 79
3
Beyond binary classification
81
3.1
Handling more than two classes
.........................81
Multi-class classification
.............................82
Multi-class scores and probabilities
......................86
3.2
Regression
.....................................91
3.3
Unsupervised and descriptive learning
....................95
Predictive and descriptive clustering
......................96
Other descriptive models
.............................100
3.4
Beyond binary classification: Summary and further reading
........102
4
Concept learning
104
4.1
The hypothesis space
...............................106
Least general generalisation
...........................108
Internal disjunction
................................110
4.2
Paths through the hypothesis space
......................112
Most general consistent hypotheses
......................116
Closed concepts
..................................116
4.3
Beyond conjunctive concepts
..........................119
using first-order logic
...............................122
4.4
Leamability
.....................................124
4.5
Concept learning: Summary and further reading
...............127
5
Tree models
129
5.1
Decision trees
...................................133
5.2
Ranking and probability estimation trees
...................138
Sensitivity to skewed class distributions
....................143
5.3
Tree teaming as variance reduction
..................... 148
Regression trees
..................................148
Contents xi
Clustering trees
..................................152
5.4
Tree models: Summary and further reading
..................155
6
Rule models
157
6.1
Learning ordered rule lists
............................158
Rule lists for ranking and probability estimation
...............164
6.2
Learning unordered rule sets
..........................167
Rule sets for ranking and probability estimation
...............173
A closer look at rule overlap
...........................174
6.3
Descriptive rule learning
.............................176
Rule learning for subgroup discovery
......................178
Association rule mining
..............................182
6.4
First-order rule learning
.............................189
6.5
Rule models: Summary and further reading
..................192
7
Linear models
194
7.1
The least-squares method
............................196
Multivariate linear regression
..........................201
Regularised regression
..............................204
Using least-squares regression for classification
...............205
7.2
The perception
...................................207
7.3
Support vector machines
.............................211
Soft margin SVM
..................................216
7.4
Obtaining probabilities from linear classifiers
................219
7.5
Going beyond linearity with kernel methods
.................224
7.6
Linear models: Summary and further reading
................228
8
Distance-based models
231
8.1
So many roads
....................................231
8.2
Neighbours and exemplars
............................237
8.3
Nearest-neighbour classification
........................242
8.4
Distance-based clustering
............................245
if-means algorithm
................................247
Clustering around medoids
...........................250
Silhouettes
.....................................252
8.5
Hierarchical clustering
..............................253
8.6
From kernels to distances
............................258
8.7
Distance-based models: Summary and further reading
...........260
xjl
Contents
9
Probabffistic
models
262
9.1
The normal
distribution
and its geometric interpretations
.........266
9.2
Probabilistic models for categorical data
....................273
Using a naive
Bayes
model for classification
..................275
Training a naive
Bayes
model
..........................279
9.3
Discriminative learning by optimising conditional likelihood
.......282
9.4
Probabilistic models with hidden variables
..................286
Expectation-Maximisation
............................288
Gaussian mixture models
.............................289
9.5
Compression-based models
...........................292
9.6
Probabilistic models: Summary and further reading
.............295
10
Features
298
10.1
Kinds of feature
..................................299
Calculations on features
.............................299
Categorical, ordinal and quantitative features
................304
Structured features
................................305
10.2
Feature transformations
.............................307
Thresholding and discretisation
.........................308
Normalisation and calibration
..........................314
Incomplete features
................................321
10.3
Feature construction and selection
.......................322
Matrix transformations
and decompositions
.................324
10.4
Features: Summary and further reading
....................327
11
Model ensembles
330
11.1
Bagging and random forests
...........................331
11.2
Boosting
.......................................334
Boosted rule learning
...............................337
11.3
Mapping the ensemble landscape
.......................338
Bias, variance and margins
............................338
Other ensemble methods
.............................339
Meta-leaming
...................................340
11.4
Model ensembles: Summary and further reading
..............341
12
Machine learning experiments
343
12.1
What to measure
............................ 344
12.2
How to measure it
.................................34g
Contents xiii
12.3
How to
interpret
it
.................................351
Interpretation of results over multiple data sets
................354
12.4
Machine learning experiments: Summary and further reading
.......357
Epilogue: Where to go from here
360
Important points to remember
363
References
367
Index
383
|
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| indexdate | 2024-07-10T00:17:18Z |
| institution | BVB |
| isbn | 9781107422223 9781107096394 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024974976 |
| oclc_num | 796214971 |
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| physical | XVII, 396 S. Ill., graph. Darst. |
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| publisher | Cambridge Univ. Press |
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| spelling | Flach, Peter Verfasser aut Machine learning the art and science of algorithms that make sense of data Peter Flach 1. publ. Cambridge Cambridge Univ. Press 2012 XVII, 396 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 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=024974976&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Flach, Peter Machine learning the art and science of algorithms that make sense of data Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Machine learning the art and science of algorithms that make sense of data |
| title_auth | Machine learning the art and science of algorithms that make sense of data |
| title_exact_search | Machine learning the art and science of algorithms that make sense of data |
| title_full | Machine learning the art and science of algorithms that make sense of data Peter Flach |
| title_fullStr | Machine learning the art and science of algorithms that make sense of data Peter Flach |
| title_full_unstemmed | Machine learning the art and science of algorithms that make sense of data Peter Flach |
| title_short | Machine learning |
| title_sort | machine learning the art and science of algorithms that make sense of data |
| title_sub | the art and science of algorithms that make sense of data |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024974976&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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