An Introduction to Machine Learning
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear a...
Κύριος συγγραφέας: | |
---|---|
Συγγραφή απο Οργανισμό/Αρχή: | |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
|
Έκδοση: | 2nd ed. 2017. |
Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 1 A Simple Machine-Learning Task
- 2 Probabilities: Bayesian Classifiers
- Similarities: Nearest-Neighbor Classifiers
- 4 Inter-Class Boundaries: Linear and Polynomial Classifiers
- 5 Artificial Neural Networks
- 6 Decision Trees
- 7 Computational Learning Theory
- 8 A Few Instructive Applications
- 9 Induction of Voting Assemblies
- 10 Some Practical Aspects to Know About
- 11 Performance Evaluation
- 12 Statistical Significance
- 13 Induction in Multi-Label Domains
- 14 Unsupervised Learning
- 15 Classifiers in the Form of Rulesets
- 16 The Genetic Algorithm
- 17 Reinforcement Learning.