Data Analysis Machine Learning and Applications Episode 2 Part 7 docx

Data Analysis Machine Learning and Applications Episode 2 Part 7 docx

Data Analysis Machine Learning and Applications Episode 2 Part 7 docx

... Identification. Socimetry, 28 , 27 7 29 9. OKADA, A. (20 03): Using Additive Conjoint Measurement in Analysis of Social Network Data. In: M. Schwaiger, and O. Opitz (Eds.): Exploratory Data Analysis in Empirical Research. ... 2 Characteristic values Actor (Family) 4 .23 3 3.418 1 Acciaiuoli 0. 129 0.134 2 Albizzi 0 .21 0 0.300 3 Barbadori 0. 179 0.053 4 Bischeri 0. 328 -0 .26...

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Data Analysis Machine Learning and Applications Episode 2 Part 8 docx

Data Analysis Machine Learning and Applications Episode 2 Part 8 docx

... (4) 12. 49 .08 (1) 09 (4) 03 (3) .06 (2) 01 (3) .03 (2) Catering 12. 17 19 (5) .03 (3) 13 .29 07 (5) 01 (3) . 12 (1) 07 (4) . 02 (2) 04 (4) .10 (2) .10 (1) Hotel facilities Location 7. 78 24 (2) .24 ... (1) 37 (3) 26 (3) Beach 9.83 10 (2) .35 (1) 5.56 09 (2) .26 (1) 25 (3) 17 (3) Hotel services Leisure activities 11. 72 20 (6) 02 (2) 7. 52 04 (6) . 02 (2) .04 (2) .20 (1)...

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Data Analysis Machine Learning and Applications Episode 2 Part 10 docx

Data Analysis Machine Learning and Applications Episode 2 Part 10 docx

... 0.00.51.01. 52. 02. 53.0 ARL 3 42. 18 341. 42 339. 42 334. 52 326 .63 316.80 306. 92 SDRL 338 .74 338. 62 338 .77 338.89 338.54 3 37. 80 3 37. 35 10 Q(.10) 38 37 35 30 22 12 5 Q(.50) 23 8 23 7 23 6 23 0 22 2 21 2 20 1 Q(.90) 78 6 ... ’000) 20 00 20 01 20 02 2003 20 04 0 25 50 75 100 125 150 Box Jenkins (# 10) SE (in ’000) 20 00 20 01 20 02 2003 20 04 0 25 5...

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Data Analysis Machine Learning and Applications Episode 2 Part 1 pot

Data Analysis Machine Learning and Applications Episode 2 Part 1 pot

... 416 P(N /2) .4 92 .24 3 .21 4 .144 .109 .24 4 . 173 .0 97 .4 42 .068 .190 .8 02 .556 .28 3 R(N /2) . 528 .139 .3 02 .1 97 .088 .1 82 .1 52 .191 .26 3 .061 .143 .71 0 .3 52 .361 29 6 Triebel et al. data point p whose ... watermark database. Table 1. Averaged precision and recall at N /2 for the watermark database. Classes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 N 322 115 139 71 9...

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Data Analysis Machine Learning and Applications Episode 2 Part 2 ppsx

Data Analysis Machine Learning and Applications Episode 2 Part 2 ppsx

... 20 01), FSG (Kuramochi and Karypis 20 01), MoSS/MoFa (Borgelt and Berthold 20 02) , gSpan (Yan and Han 20 02) , Closegraph (Yan and Han 20 03), FFSM (Huan et al. 20 03), and Gaston (Nijssen and Kok 20 04). A ... (1988b): LM A O = [ˆu  W 2 ˆu/ ˆ V 2 ] 2 T 22 −(T 21 A ) 2 ˆvar( ˆ U) , (6) LM A U = [ˆu  B  BW 1 y] 2 H rho −H TU ˆvar( ˆ T)H  TU , (7) where T...

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Data Analysis Machine Learning and Applications Episode 2 Part 3 pps

Data Analysis Machine Learning and Applications Episode 2 Part 3 pps

... asoundex.lname 11 525 6 WESTERHEIDE W236 20 0001 BESTEWEIDE B233 20 00 02 WESTERWELLE W236 3.3 Candidate selection candidates (data1 , data2 , method, selvars1, selvars2, key1, key2, ) provides an interface ... 89.4% 98 .2% FastICA Mole 84.5% 87. 7% 56.1% Melanoma 89.4% 89.4% 98 .2% Pre-Proc. Class OAA-SVM OAO-SVM unbalanced SVM FlexICA Mole 72 . 7% 69.9% 87. 7% Melanoma 92. 9% 94 ....

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Data Analysis Machine Learning and Applications Episode 2 Part 4 doc

Data Analysis Machine Learning and Applications Episode 2 Part 4 doc

... 029 911839 J4.8(cv) 408911941 RPart0 56 071 073 5 RPart1 64108 625 QUEST 422 50 72 0 CTree 76 7890 37  26 20 27 38 49 37 Complexity J4.8 J4.8(cv) RPart0 RPart1 QUEST CTree  J4.8 010 020 3 J4.8(cv) 17 0 0 0 5 3 25 RPart0 18 18 0 0 13 15 64 RPart1 18 18 16 ... other use is made of such data than statistical analysis. 1 2 3 4 5 6 7 8 9 10 11 12 0 0.5 1 1.5 2 2.5 3 3.5 4...

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Data Analysis Machine Learning and Applications Episode 2 Part 5 pps

Data Analysis Machine Learning and Applications Episode 2 Part 5 pps

... 20 30 40 50 60 70 80 0 0 .2 0.4 0.6 0.8 1 Typical day 12 0 5 10 15 20 25 0 1 2 3 4 5 x 10 7 0 5 1 0 1 5 2 0 2 5 0 2 4 6 8 10 x 10 6 cluster 6, application: p2p down ( 12% ) volume (in byte) global ... number unknown up unknown down p2p up p2p down web up web down 0 5 10 15 20 25 0 1 2 3 4 5 6 x 10 6 0 5 1 0 1 5 2 0 2 5 0 0.5 1 1.5 2 2.5 x 10 6 Typical day 12 for...

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Data Analysis Machine Learning and Applications Episode 2 Part 6 potx

Data Analysis Machine Learning and Applications Episode 2 Part 6 potx

... 361 108560 1 826 10936 026 285183 1816 923 1 021 51 1948 123 47 02 1 924 30044 079 003 526 98 1858 24 71 98 924 00 1898 26 326 628 200005145 177 5 22 800903 921 9106010056 1 922 70 4600 1969 976 1860 18 47 1 826 181619481 924 18581898 177 51 922 196918 47 108560 1 826 10936 026 285183 1816 923 1 021 51 1948 123 47 02 1 924 30044 079 003 526 98 1858 24 71 98 924 00 1898...

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Data Analysis Machine Learning and Applications Episode 2 Part 9 pdf

Data Analysis Machine Learning and Applications Episode 2 Part 9 pdf

... pseudo R 2 1 -131.49 28 0. 97 28 9. 97 .00 .23 2 -1 17. 04 27 6.09 29 7. 09 .09 .81 3 -100.96 26 7. 92 300. 92 .08 . 92 4 -89 .76 26 9. 52 314. 52 .11 . 92 5 - 82. 62 279 .24 336 .24 .11 .95 Classifying Contemporary ... biased T 04s 8.93 2. 10 2. 59 biased T 05s 10.59 -8 .75 -4 .70 biased TM score 3. 67 4.05 .88 10 .29 DM score 2. 71 1.03 -7. 87 8.94 EM score -2. 4...

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