| Description |
1 online resource (xvii, 396 pages) : color illustrations |
| Contents |
Cover; MACHINE LEARNING: The Art and Science of Algorithms that Make Sense of Data; Title; Copyright; Dedication; Brief Contents; Contents; Preface; How to read the book; Acknowledgements; Prologue: A machine learning sampler; CHAPTER 1 The ingredients of machine learning; 1.1 Tasks: the problems that can be solved with machine learning; Looking for structure; Evaluating performance on a task; 1.2 Models: the output of machine learning; Geometric models; Probabilistic models; Logical models; Grouping and grading; 1.3 Features: the workhorses of machine learning; Two uses of features |
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Feature construction and transformationInteraction between features; 1.4 Summary and outlook; What you'll find in the rest of the book; CHAPTER 2 Binary classification and related tasks; 2.1 Classification; Assessing classification performance; Visualising classification performance; 2.2 Scoring and ranking; Assessing and visualising ranking performance; Turning rankers into classifiers; 2.3 Class probability estimation; Assessing class probability estimates; Turning rankers into class probability estimators; 2.4 Binary classification and related tasks: Summary and further reading |
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CHAPTER 3 Beyond binary classification3.1 Handling more than two classes; Multi-class classification; Multi-class scores and probabilities; 3.2 Regression; 3.3 Unsupervised and descriptive learning; Predictive and descriptive clustering; Other descriptive models; 3.4 Beyond binary classification: Summary and further reading; CHAPTER 4 Concept learning; 4.1 The hypothesis space; Least general generalisation; Internal disjunction; 4.2 Paths through the hypothesis space; Most general consistent hypotheses; Closed concepts; 4.3 Beyond conjunctive concepts; Using first-order logic |
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4.4 Learnability4.5 Concept learning: Summary and further reading; CHAPTER 5 Tree models; 5.1 Decision trees; 5.2 Ranking and probability estimation trees; Sensitivity to skewed class distributions; 5.3 Tree learning as variance reduction; Regression trees; Clustering trees; 5.4 Tree models: Summary and further reading; CHAPTER 6 Rule models; 6.1 Learning ordered rule lists; Rule lists for ranking and probability estimation; 6.2 Learning unordered rule sets; Rule sets for ranking and probability estimation; A closer look at rule overlap; 6.3 Descriptive rule learning |
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Rule learning for subgroup discoveryAssociation rule mining; 6.4 First-order rule learning; 6.5 Rule models: Summary and further reading; CHAPTER 7 Linear models; 7.1 The least-squares method; Multivariate linear regression; Regularised regression; Using least-squares regression for classification; 7.2 The perceptron; 7.3 Support vector machines; Soft margin SVM; 7.4 Obtaining probabilities from linear classifiers; 7.5 Going beyond linearity with kernel methods; 7.6 Linear models: Summary and further reading; CHAPTER 8 Distance-based models; 8.1 So many roads |
| Summary |
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook |
| Bibliography |
Includes bibliographical references (pages 367-381) and index |
| Notes |
8.2 Neighbours and exemplars |
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Print version record |
| Subject |
Machine learning -- Textbooks
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COMPUTERS -- Enterprise Applications -- Business Intelligence Tools.
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COMPUTERS -- Intelligence (AI) & Semantics.
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Machine learning
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Apprentissage automatique -- Manuels scolaires.
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| Genre/Form |
Textbooks
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| Form |
Electronic book
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| ISBN |
9781139571227 |
|
1139571222 |
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9781139569415 |
|
1139569414 |
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9780511973000 |
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0511973004 |
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9781139570312 |
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1139570315 |
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1107096391 |
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9781107096394 |
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9781139572972 |
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1139572970 |
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