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Manuels d'enseignement supérieur
|Additional Physical Format:||Print version:
Flach, Peter A.
Cambridge ; New York : Cambridge University Press, 2012
|Material Type:||Document, Internet resource|
|Document Type:||Internet Resource, Computer File|
|All Authors / Contributors:||
Peter A Flach
|ISBN:||9781139571227 1139571222 9781139569415 1139569414 9780511973000 0511973004 9781139570312 1139570315 1107096391 9781107096394 9781139572972 1139572970|
|Notes:||8.2 Neighbours and exemplars.|
|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. 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. 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. 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. 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.|
"This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the
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