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-   -   Exercise 4.4 (http://book.caltech.edu/bookforum/showthread.php?t=4611)

prithagupta.nsit 08-15-2015 08:15 AM

Exercise 4.4
 
For Exercise 4.4

I am not able to understand that in this exercise 4.4, what is actually w and do we have to consider the regularizatio also and if the formulae:
Ein(w) =1/N (Zwlin − y)T * (Zwlin − y)

how can w-wlin come in picture.
by this formula I am able to get the second term but not the second term?
Can anyone help me derive this expression or can anyone share his/her solution with me.

http://book.caltech.edu/bookforum/showthread.php?t=4512
same is the Ein used here??? is it out of sample error... I am not able to understand the conflict. What my understanding is that we have derived wlin but for a variable vector we first check how much does it vary from the wlin and multiplied by Z vector gives us how much function vary from average hypothesis and the second term gives us the error of Wlin predicting outputs. Is my understanding right??

magdon 08-24-2015 06:04 AM

Re: Exercise 4.4
 
E_{in}(w) is given in eq. (4.2). The exercise is asking you to rewrite this expression using w_{lin} and H as defined in the problem.

w is a variable parameter, it is not known. It is to be obtained by optimizing E_{in}(w).


Quote:

Originally Posted by prithagupta.nsit (Post 11998)
For Exercise 4.4

I am not able to understand that in this exercise 4.4, what is actually w and do we have to consider the regularizatio also and if the formulae:
Ein(w) =1/N (Zwlin − y)T * (Zwlin − y)

how can w-wlin come in picture.
by this formula I am able to get the second term but not the second term?
Can anyone help me derive this expression or can anyone share his/her solution with me.

http://book.caltech.edu/bookforum/showthread.php?t=4512
same is the Ein used here??? is it out of sample error... I am not able to understand the conflict. What my understanding is that we have derived wlin but for a variable vector we first check how much does it vary from the wlin and multiplied by Z vector gives us how much function vary from average hypothesis and the second term gives us the error of Wlin predicting outputs. Is my understanding right??



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