Book contents
- Frontmatter
- Contents
- Preface
- Prologue: A machine learning sampler
- 1 The ingredients of machine learning
- 2 Binary classification and related tasks
- 3 Beyond binary classification
- 4 Concept learning
- 5 Tree models
- 6 Rule models
- 7 Linear models
- 8 Distance-based models
- 9 Probabilistic models
- 10 Features
- 11 Model ensembles
- 12 Machine learning experiments
- Epilogue: Where to go from here
- Important points to remember
- References
- Index
10 - Features
Published online by Cambridge University Press: 05 November 2012
Book contents
- Frontmatter
- Contents
- Preface
- Prologue: A machine learning sampler
- 1 The ingredients of machine learning
- 2 Binary classification and related tasks
- 3 Beyond binary classification
- 4 Concept learning
- 5 Tree models
- 6 Rule models
- 7 Linear models
- 8 Distance-based models
- 9 Probabilistic models
- 10 Features
- 11 Model ensembles
- 12 Machine learning experiments
- Epilogue: Where to go from here
- Important points to remember
- References
- Index
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- Machine LearningThe Art and Science of Algorithms that Make Sense of Data, pp. 298 - 329Publisher: Cambridge University PressPrint publication year: 2012
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