Review
Classic Machine Learning Methods
In: Machine Learning for Brain Disorders [Internet]. New York, NY: Humana; 2023. Chapter 2.
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Affiliations
- PMID: 37988511
- Bookshelf ID: NBK597496
- DOI: 10.1007/978-1-0716-3195-9_2
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Review
Classic Machine Learning Methods
Excerpt
In this chapter, we present the main classic machine learning methods. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest neighbor methods, linear and logistic regressions, support vector machines, and tree-based algorithms. We also describe the problem of overfitting as well as strategies to overcome it. We finally provide a brief overview of unsupervised learning methods, namely, for clustering and dimensionality reduction. The chapter does not cover neural networks and deep learning as these will be presented in Chaps.
Copyright 2023, The Author(s).
Sections
- 1. Introduction
- 2. Notations
- 3. Nearest Neighbor Methods
- 4. Linear Regression
- 5. Logistic Regression
- 6. Overfitting and Regularization
- 7. Penalized Models
- 8. Support Vector Machine
- 9. Multiclass Classification
- 10. Decision Functions with Normal Distributions
- 11. Tree-Based Methods
- 12. Clustering
- 13. Dimensionality Reduction
- 14. Kernel Methods
- 15. Conclusion
- Acknowledgements
- References
References
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- Murphy KP (2012) Machine learning: a probabilistic perspective. The MIT Press, Cambridge, MA
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- Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509–517
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- Omohundro SM (1989) Five balltree construction algorithms. Tech. rep., International Computer Science Institute
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- Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin