X: D E T E R M I N E Y: > d 0 i > t 1 − < R > m 0 x < n < − !" # $%&'() ! "### $ ! "### "%& * "! "' ( ) *+ %##" ! , ! " # $%& !& & '(() y1 y3 y4 y5 y6 y7 ! -+ . / 0 1 , /,2 0 3 4 y2 0 3 0¼ ¼ ¼ * + ! " # + , ,- . 5 + 5 + 1 -+ - / 01 *2 5 + - , -+ - --- 1--67 7+ , ! " # 3 3 3 8, $ 9 2 # !" + : ; : < : %##% 4 / , * , - / 9 = > 2 3 4 4 4 ? @ A 2 ? 3 ½ B ? " , - * 3 3 " 2 3 " ? 3 3 C? $ D 3 4 $ ½ 3 4 $ * % , - !"# 1 1 + 5 1" 3 * "EF& + 2 3 0 3 ! ? 3 3 C0 4 0 D C0 4 0 D , - + !"# ' 3 # 3 " 3 " 3 " ? 3 3 3 C? 3 4 $ 72 7 9 $ D + " !"# s1 s4 s18 0.324 ! 3 #G%H 3 < 1 >./ + 9 "& H# &H# >5 * I "## & >5 " . . ! / & 01 1 '( Regression Tree CRF vs. Recurrent Decision Tree for word prediction 35 Viterbi CRF Greedy CRF Recurrent Decision Tree 30 % correct words 25 20 15 10 5 0 50 100 Iterations 150 200 . 2 + 3 4 Regression Tree CRF using 3 & 7 letter windows for word prediction 45 Boosted Regression Trees w/ 3 letter window Boosted Regression Trees w/ 7 letter window Recurrent Decision Tree w/ 3 letter window Recurrent Decision Tree w/ 7 letter window 40 % correct words 35 30 25 20 15 10 5 0 50 100 Iterations 150 200 *% + 5 Regression Tree CRF using 3 & 7 letter windows for letter prediction 85 Boosted Regression Trees w/ 3 letter window Boosted Regression Trees w/ 7 letter window Recurrent Decision Tree w/ 3 letter window Recurrent Decision Tree w/ 7 letter window % correct letters 80 75 70 65 60 0 50 100 Iterations 150 200 .% , - $ 6 02 5 ! + 9 + - ! +#, ! 7 >./ : >./ >J> Æ +# "
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