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d.IO CitxTxD2 Cx5 CxY Old linear time better than OCdD Products - PDF document

of as HI tt It t XOR Tinear WT X predictor WT cffx E feature expansion Feature Expansion e Rd Quadratic Expansion x Xi XI DIX EXd I Exa x IX Xz DIX Xz d2Xd i.X d.IO CitxTxD2 Cx5 CxY Old linear time better than OCdD Products of


  1. of as HI tt It t XOR Tinear WT X predictor WT cffx E feature expansion Feature Expansion e Rd Quadratic Expansion x Xi XI DIX EXd I Exa x IX Xz DIX Xz d2Xd i.X d.IO CitxTxD2 Cx5 CxY Old linear time better than OCdD Products of all subsets Cx X 2 Its SEED Xi XD Xd Xd i XD X XX Xie c ped d

  2. It citing 465 D How hard f Old Old time 465 expf HIEI Gaussian kernel Old linear 44 per exptI lnxER.EC EzfEI kernel K XXX R kernel is A a symmetric function such that XnEX Xi for any function G the Gram matrix klx.mx JEqiir is positive semidefinite I IRn KC a kernel is show How to Easy way Find such that of mapping kCxxD I similarity measure new features C

  3. definition the implies Gig dCxi5dCxg G E'Io Io Axis xD P.s.cl Io 8 220 GTE off tf Examples Ktx x Xix this useful when is need CI xtx.dk kcx.xj Polynomial Gaussian Kerney RBF Radial Basis Function z t kCxixD exp 282 note in the other examples SVM Dual 9 Exp 8 I fea Zai ma ax KCxing OE The C S't For RBF Pf lives in w w't EI Yi Xi okra

  4. X For test point a new 1 fxz54td wittol Ii y kcx.mx kerned 7 c similarity kernels from old Build kernels new ktx.yj for ckf.my so c kernel k t Kday Klay Kathy If EkCx.y1 ker.no kcCx.y kafx.y other in examples notes kernTRidgeRgessin be D I XIN A b if Ridge Reg CATA form Alternative GIFHAIYAT i foerenaoteproof AT CGT AID to GEIR Gigs Xj I

  5. tE0GaToag ATCGIE.iq ii v Ziva Xi Atv w X New test point EV.x xtid X.am prediction I kcx.x.jo 01651 Z VioCxTofk lx5ii taxi

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