Regression

Recommended Literature

If you want to read up further on this topic, the following material is recommended.

Bishop. Pattern recognition and machine learning. 2006.

• Section 3.1 Linear Basis Function Models, and

• Section 3.2. Bias-Variance Decomposition

Friedman, Hastie, and Tibshirani. The elements of statistical learning. 2001

• Sections 2.6 on Statistical Models, Supervised Learning and Function Approximations

• Section 2.9 on Model Selection and Bias-Variance Tradeoff, and

• Chapter 3 on Linear Methods for Regression (in particular sections 3.1 and 3.2).