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Linear Regression Models in PRML by 20Z367 - HARDIK P is a document available to read on EtoBox.
The chapter discusses linear regression models that use a linear combination of basis functions to model the relationship between input variables x and target variables t. It covers maximum likelihood and least squares estimation for fitting the regression weights, and introduces regularization to reduce overfitting. The bias-variance decomposition is explained as a means of understanding the tradeoff between model complexity and accuracy. Bayesian linear regression is also discussed as a way to place a pro
- Author
- 20Z367 - HARDIK P
- Language
- EN