Springer Series in Statistics(共48册), 这套丛书还有 《Monte Carlo Strategies in Scientific Computing》《Statistical Demography and Forecasting》《Interpolation of Spatial Data》《Smoothing Methods in Statistics》《Principles and Theory for Data Mining and Machine Learning》 等。
The Element of Statistical Learning – Chapter 5 oxstar@SJTU January 6, 2011 Ex. 5.9 Derive the Reinsch form Sλ = (I + λK)?1 for the smoothing spline. Answer Let K = (NT )?1 ?N N?1 , so K does not depend on λ, and we have ?N = NT KN Sλ = N(NT N + λ?N ...
3whereℓi(x0) is the i-th element of the N-dimensional column vector X(XTX)−1x0,as stated at the bottom of page 24 of the book. We now consider Equations 2.27 and 2.28.The variation is over all training sets T,and over all values of y0,while keeping x0,xed.Note that x0...
The Element of Statistical Learning – Chapter 2 oxstar@SJTU January 3, 2011 Ex. 2.1 Suppose each of K-classes has an associated target tk, which is a vector of all zeros, except a one in the kth position. Show that classifying to the largest element of y? amounts to choosing the ...
16 February 2013 Introduction The Elements of Statistical Learning is an in?uential and widely studied book in the ?elds of machine learning, statistical inference, and pattern recognition. It is a standard recommended text in many graduate courses on these topics. It is also very challenging, ...
TheElementofStatisticalLearning–Chapter2 oxstar@SJTU January3,2011 Ex.2.1SupposeeachofK-classeshasanassociatedtargettk,whichisavectorofallzeros,exceptaoneinthekthposition.Showthatclassifyingtothelargestelementofˆyamountstochoosingtheclosesttarget(mink)tk−ˆy,,iftheelementsofˆysumtoone. ProofOurgo...
统计学习[The Elements of Statistical Learning]第六章习题
1、 # TheElementofStatisticalLearningChapter2 HYPERLINK mailto:oxstarS.JTUoxstarS.JTUJcinuciry3?2011Ex.2.1SupposeeachofclasseshasanassociatedtargetwhichisavectorofallzerosexceptnoneintheA:thpositionShowthatclassifyingtothelargestekunentofynmountstochoosingtheclosesttarget,mi】以训,iftheelementsofysumtoone 2...
The Element of Statistical Learning –Chapter 8 oxstar@SJTU January 6,2011 Ex.8.1Let r (y )and q (y )be probability density functions.Jensen’s inequality states that for a random variable X and a convex function φ(x ),E[φ(X )]≥φ[E(X )].Use Jensen’s inequality to show ...
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