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I was reading "The Elements of Statistical Learning Book by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie" where I encountered the following:

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The part tells us that the RSS criterion will lead us to infinitely many solutions and in order to obtain a unique solution we need to put some restrictions on the RSS criterion.

The book further discusses the following methods to impose such restrictions.

  • Roughness penalty
  • Kernel methods
  • Basis Functions and Dictionary Methods

I very specifically want to understand:

  • These methods with some simple examples
  • The intuition behind each of these methods
  • Please suggest an appropriate source for better understanding

Thank you!

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    $\begingroup$ You have to explain better what you do not understand. Dumping three pages of a book on a post does not help. $\endgroup$ – Xi'an Feb 1 at 8:45

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