This preview shows page 1. Sign up to view the full content.
Unformatted text preview: np ) ( A n , B n ) = (1 , 0) consider the following classiﬁcation scheme: for a new sample x new = ( x 1 , ..., x p ), choose h and calculate ˆ A ( x new ) = ∑ n i =1 K (  X ix new  /h ) A i ∑ n i =1 K (  X ix new  /h ) and ˆ B ( x new ) = ∑ n i =1 K (  X ix new  /h ) B i ∑ n i =1 K (  X ix new  /h ) Deﬁne the probability that x new ∈ A and B respectively as p A ( x new ) = exp( ˆ A ( x new )) exp( ˆ A ( x new )) + exp( ˆ B ( x new )) , p B ( x new ) = exp( ˆ B ( x new )) exp( ˆ A ( x new )) + exp( ˆ B ( x new )) We classify x new ∈ A if p A ( x new ) > p B ( x new ) , and x new ∈ B otherwise. Consider the banknotes data with ( training set and ( validation set , with h = 1 what is the classiﬁcation error? try diﬀerent h . (CODE) 1...
View
Full
Document
This note was uploaded on 10/04/2010 for the course STAT ST4240 taught by Professor Xiayingcun during the Fall '09 term at National University of Singapore.
 Fall '09
 XIAYingcun
 Linear Regression

Click to edit the document details