Abstract
There are statistical learning procedures not discussed earlier that can be framed as regression analysis and deserve a least a brief conceptual overview. These include neural networks, Bayesian additive regression trees, and reinforcement learning. We consider these now introducing also a bit of material on “deep learning.” Deep learning is evolving so rapidly, that the discussion of deep learning may be somewhat dated.
The original version of this chapter was revised: See the “Chapter Note” section at the end of this chapter for details. The erratum to this chapter is available at https://doi.org/10.1007/978-3-319-44048-4_10.
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Notes
- 1.
An excellent introductory lecture on neural nets by Patrick Winston of MIT can be found at http://teachingexcellence.mit.edu/inspiring-teachers/patrick-winston-6-034-lecture-12-learning-neural-nets-back-propagation. If one is willing to learn a somewhat different notational scheme, Christopher Bishop’s treatment is superb (2006: Chap. 5).
- 2.
The color coding of the arrows in Fig. 8.1 is meant to indicate that each hidden layer has its own set of weights. These weights will typically differ from one another; the set of \(\alpha _{m}\) will typically differ. Their common color is not meant to convey that the weights are the same.
- 3.
Wu, Tjelmeland and West (2007) define a “pinball prior” for tree generation. The pinball prior and Fig. 8.2 have broadly similar intent, but the details are vastly different.
- 4.
Readers interested in running genetic algorithms in R should consider using the library GA written by Luca Scrucca (Scrucca 2013).
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Berk, R.A. (2016). Some Other Procedures Briefly. In: Statistical Learning from a Regression Perspective. Springer Texts in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-44048-4_8
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DOI: https://doi.org/10.1007/978-3-319-44048-4_8
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