Data Analysis Machine Learning and Applications Episode 3 Part 7 ppt

Data Analysis Machine Learning and Applications Episode 3 Part 9 docx

Data Analysis Machine Learning and Applications Episode 3 Part 9 docx

... R., 31 9 Bessler, Wolfgang, 499 Biemann, Chris, 577 Borgelt, Christian, 2 29 Bradley, Patrick E., 95 Brunner, Gerd, 237 Brusch, Michael, 431 Burgard, Wolfram, 2 69, 2 93 Burkhardt, Hans, 11, 37 , 237 Calò, ... Wendelin, 2 69 Fernández-Aguirre, K., 1 83 Fessant, F., 34 3 Fiedler, Mathias, 2 29 Flodman, Pamela, 1 19 Franke, Markus, 35 5 Fried, Roland, 277 Gabriel, Thomas R.,...

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Data Analysis Machine Learning and Applications Episode 1 Part 3 docx

Data Analysis Machine Learning and Applications Episode 1 Part 3 docx

... 14 :55. 23 10 :55.70 14 : 21. 99 1. 37 1. 04 Classification Time 03 : 13 .60 00 :14 . 73 00 :14 . 63 13 .14 13 . 23 Classif. Accuracy % 95.78 % 91. 01 % 91. 01 % 1. 05 1. 05 USPS RBF H1-SVM H1-SVM RBF/H1 RBF/H1 (Min-Max) Kernel ... 2.62 3. 87 77 .30 46.67 2 28. 83 88. 41 18.06 2.50 1 68.54 7.44 2.54 0.00 SRNG 1 2 3 4 4 0.00 0.56 2.08 53. 33 3 0.67 3. 60 81. 12 44 .1...

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Data Analysis Machine Learning and Applications Episode 1 Part 4 pptx

Data Analysis Machine Learning and Applications Episode 1 Part 4 pptx

... 0.89 642 0.763 84 0. 712 12 0.85838 ¯r 0.5 313 0 0 .4 41 1 9 0.56066 0 .44 540 0 .45 403 0.39900 0. 618 83 0. 747 30 ccr 98.22% 98.00% 94. 44% 90.67% 97 .11 % 89.56% 98.89% 98 .44 % 11 a 0. 043 35 0. 043 94 0.00 012 0. 043 88 ... 0. 547 46 0.6 013 9 0.27 610 0 .46 735 0.58050 0 .49 842 0.33303 0.5 017 8 b 0. 910 71 0. 848 88 0 .48 550 0.73720 0. 813 17 0.79 644 0.72899 0. 744 62 6 a 0. 6...

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Data Analysis Machine Learning and Applications Episode 1 Part 7 doc

Data Analysis Machine Learning and Applications Episode 1 Part 7 doc

... follows: E (t +1) |···∼W  2G +2gD,(2h+ 2 K  k =1 6 (t) 1 k ) 1  , S (t +1) |···∼D(J+ n 1 , ,J +n K ), z (t +1) k |···∼N  (n k 6 (t) 1 k + <) 1 (n k 6 (t) 1 k y k + <[),(n k 6 (t) 1 k + <) 1  , 6 1( t +1) k |···∼W ⎛ ⎝ 2D ... + (1+ m 1 ) ˆ B, and a 95% confidence interval for Q is given by ˆ Q±t Q,0. 975 ˆ T 1/ 2 , where the degrees of freedom are Q =(m...

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Data Analysis Machine Learning and Applications Episode 1 Part 10 ppt

Data Analysis Machine Learning and Applications Episode 1 Part 10 ppt

... 1 1 1 1 ∗ c4–g4 1 1 1 1 1 c4–a4 1 1 1 1 1 c4–c5 1 1 0 1 ∗ 1 instrument notes flu guit pian trum viol c4–c4 0 0 1 ∗ 1 ∗ 1 c4–e4 1 1 1 1 1 ∗ c4–g4 1 1 1 1 1 c4–a4 1 1 1 1 1 c4–c5 1 1 1 ∗ 1 ∗ 1 4.3 ... 1 1 1 1 1 c4–e4 0 1 0 0 1 c4–g4 0 0 0 0 0 c4–a4 1 1 1 0 0 c4–c5 1 1 1 1 1 instrument notes flu guit pian trum viol c4–c4 1...

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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

... preparation (data= d1, variable='lname', method='asoundex') lname asoundex.lname 11 525 6 WESTERHEIDE W 236 20 0001 BESTEWEIDE B 233 20 00 02 WESTERWELLE W 236 3. 3 Candidate selection candidates (data1 , ... retains only 83 candidates. > candidates (data1 =d1.prep, data2 =d2.prep, method='blocking',selvars1='asoundex.lname') > candidates (...

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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 3 Part 1 pdf

Data Analysis Machine Learning and Applications Episode 3 Part 1 pdf

... 1 035 11 4 15 9 13 17 158 0 . 31 4 Polynomial (3rd degree) 12 42 633 516 14 767 17 158 0.6 43 RBF 860 6 51 498 15 149 17 158 0.640 Coulomb 0 11 24 25 16 009 17 158 0 .14 8 M1 287 850 299 15 722 17 158 0.505 M2 * 19 1 ... Me NonV B T3 Ratios VB T2 NonV B T2 VB T3 NonV B T3 Test1 Test2 ROS 9.52 3. 93 5 .39 2 .16 1 03 11 6 ROE 6.7 3. 83 3 .3 2. 01 111 11 0 ROI 6.85...

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Data Analysis Machine Learning and Applications Episode 3 Part 2 pdf

Data Analysis Machine Learning and Applications Episode 3 Part 2 pdf

... Artificial Intelligence, pp. 43 52, July 1998. BURKE, R. (20 02) : Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction. vol. 12( 4), pp. 33 1 37 0. HERLOCKER, J.L., ... EachMovie, containing 2, 558,871 votes from 61, 1 32 users on 1,6 23 movies, and the MovieLens100k dataset, contain- ing 100,000 ratings from 9 43 users on 1,6 82 movies. Th...

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Data Analysis Machine Learning and Applications Episode 3 Part 3 pps

Data Analysis Machine Learning and Applications Episode 3 Part 3 pps

... Weibull K=2 K =3 K=4 K=5 separate 233 39.27 232 02. 23 230 40.01 229 43. 11 main.g 233 55.66 230 58.25 22971.86 228 63. 43 main.p 235 03. 73 233 68.77 231 65.60 230 68.47 int.gp 235 72.21 234 22.51 233 05. 63 230 75.76 main.gp ... 128 5 4 4 4 120 120 126 3 2 15 10 13 105 108 107 32 322229497 93 436 3029747979 5414 233 676564 ±10 234 3 231 787075 35 9 535 4455142 48564672 435 19 590...

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Data Analysis Machine Learning and Applications Episode 3 Part 4 potx

Data Analysis Machine Learning and Applications Episode 3 Part 4 potx

... Equation 3 and Figure 4: P nìm = U nìc ÃS cìc ÃV cìm (3) 2.69 0.57 2.22 4. 25 0.78 3. 93 2.21 0. 04 3. 17 1 .38 2.92 4. 78 P nìi -0.61 0.28 -0.29 -0.95 -0. 74 0. 14 U nìc 8.87 0 0 4. 01 S cìc -0 .47 -0.28 ... efc(0.17) Portal 1 0.1126 0.20 54 0.2815 0 .35 18 0. 640 8 0.7685 0 .36 Portal 2 0. 142 5 0.2050 0.1 836 0.2079 0.1965 0. 233 8 0.18 Portal 3 0.0058 0. 245 5 0.02...

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Data Analysis Machine Learning and Applications Episode 3 Part 5 pdf

Data Analysis Machine Learning and Applications Episode 3 Part 5 pdf

... weight- ings. Rand cRand k tf tf-idf tf tf-idf 3 0.48 0.49 0. 03 0. 03 4 0 .51 0 .52 0. 03 0. 03 5 0 .54 0. 53 0.02 0.02 6 0 .55 0 .56 0.02 0. 03 Average 0 .52 0 .52 0.02 0. 03 ments are rather low, indicating that ... the percentage identified by humans. Senate size 03 59 Documents 0 255 739 0 Percentage 0.000 25. 654 74 .34 6 0.000 Human Percentage 2.116 27 .30 6 70 .55 1 0.02...

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Data Analysis Machine Learning and Applications Episode 3 Part 6 doc

Data Analysis Machine Learning and Applications Episode 3 Part 6 doc

... story The empirical tests on the data from the 66 texts support our hypothesis with good and very good 2 values. Figures 2 and 3 show typical graphs of the theoretical and empirical distributions ... information. Figure 6 shows the relationship between the parameters b and c. Quantitative Text Analysis Using L-, F- and T-Segments 63 9 Table 1. Text numbers in the corpus w...

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Data Analysis Machine Learning and Applications Episode 3 Part 7 ppt

Data Analysis Machine Learning and Applications Episode 3 Part 7 ppt

... Measurements and the corresponding ranks of MnO Value 0.01 0.02 0. 03 0.04 0.05 0.06 0. 07 0.08 0.09 0.10 0.11 0. 13 Frequency 171 82 071 54 231 11 Rank 9 26.5 45.5 59 63 66 70 .5 73 . 5 76 78 79 80 Transformation ... <T1– 07& gt; -0 .70 7- Geographic treatment <T1– 070 7> 7- North America <na4r7span:T1– 070 1-T1– 070 9:T2 7& gt; 73 United States <na4r7span:T1–...

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Data Analysis Machine Learning and Applications Episode 3 Part 8 doc

Data Analysis Machine Learning and Applications Episode 3 Part 8 doc

... Linguistics, 637 , 655 Question Answering, 5 53 R, 33 5, 38 9, 569 Rank Data, 681 Recommender Systems, 525, 533 , 541, 619 Record Linkage, 33 5 Reference Modelling, 37 3 Regression, 36 3 Relationships, 36 3, 629 Return ... Segmentation, 479 Data Analysis, 31 9 Data Augmentation, 111 Data Depth, 455 Data Integration, 33 5 Data Mining, 421 Data Quality, 33 5 Data Tran...

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