Abstract
Chapter 2 introduced the concepts and formulation developed in the context of the Statistical Learning Theory. In this chapter, those concepts are illustrated using the following algorithms: Distance-Weighted Nearest Neighbors, Perceptron, Multilayer Perceptron, and Support Vector Machines.
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Notes
- 1.
We remind the Bayes classifier is the best possible in the whole space of functions \(\mathcal {F}_{\text{all}}\).
References
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L. de Carvalho Pagliosa, R.F. de Mello, Applying a kernel function on time-dependent data to provide supervised-learning guarantees. Expert Syst. Appl. 71, 216–229 (2017)
R.F. de Mello, M.D. Ferreira, M.A. Ponti, Providing theoretical learning guarantees to deep learning networks, CoRR, abs/1711.10292 (2017)
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Fernandes de Mello, R., Antonelli Ponti, M. (2018). Assessing Supervised Learning Algorithms. In: Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-94989-5_3
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DOI: https://doi.org/10.1007/978-3-319-94989-5_3
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