# DMtutorial9 - h and calculate ˆ A x new = ∑ n i =1 K ||...

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Tutorial 10 1. Both linear regression model and separating hyperplane in classiﬁcation (in e.g. SVM) are looking for a linear combination of covariates. Explain their diﬀerence in the estimation and the rules in prediction. 2. we can use support vector machine learning for function estimation. Consider the motorcycle data. plot the ﬁtted curve. discuss the role of gamma in SVM. 3. For the leukemia gene expression data ( (training points) . Use sample 11—33 as training set and the others as validation set, compare SVM and FDA. 4. For data set X Y ( classes ) X 1 = ( x 11 ,...,x 1 p ) ( A 1 ,B 1 ) = (0 , 1) X 2 = ( x 21 ,...,x 2 p ) ( A 2 ,B 2 ) = (0 , 1) ... X n = ( x n 1 ,...,x np ) ( A n ,B n ) = (1 , 0) consider the following classiﬁcation scheme: for a new sample x new = ( x 1 ,...,x p ), choose
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Unformatted text preview: h and calculate ˆ A ( x new ) = ∑ n i =1 K ( || X i-x new || /h ) A i ∑ n i =1 K ( || X i-x new || /h ) and ˆ B ( x new ) = ∑ n i =1 K ( || X i-x new || /h ) B i ∑ n i =1 K ( || X i-x 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 . 1...
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## 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.

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