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Machine learning: the art and science of algorithms that make sense of data

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Bibliographic Details
Main Author:	Flach, Peter (Author)
Format:	Book
Language:	English
Published:	Cambridge Cambridge Univ. Press 2012
Edition:	1. publ.
Subjects:	Maschinelles Lernen
Online Access:	Inhaltsverzeichnis
Item Description:	Hier auch später erschienene, unveränderte Nachdrucke
Physical Description:	XVII, 396 S. Ill., graph. Darst.
ISBN:	9781107422223 9781107096394

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MARC


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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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language	English
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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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