Data Analysis Machine Learning and Applications Episode 3 Part 3 pps

Data Analysis Machine Learning and Applications Episode 1 Part 1 doc

Data Analysis Machine Learning and Applications Episode 1 Part 1 doc

... able2 14 4 42 32 8 44 46 2 24 38 9 11 20 16 15 6 21 50 13 30 27 49 1 5 29 28 34 7 35 22 3 31 37 48 12 26 39 10 45 17 23 25 75 98 18 43 36 33 19 47 90 70 82 71 41 40 57 78 94 84 58 88 79 59 55 51 91 73 85 64 61 65 62 80 96 89 83 95 10 0 63 54 74 92 53 72 87 76 93 97 66 81 69 67 99 11 3 68 86 56 60 77 52 13 9 13 4 13 0 10 3 13 8 10 9 14 0 14 3 11 4...

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Data Analysis Machine Learning and Applications Episode 1 Part 2 potx

Data Analysis Machine Learning and Applications Episode 1 Part 2 potx

... Proc. of 26 th DAGM-Symposium. Springer, 22 0 22 7. HAASDONK, B. and BURKHARDT, H. (20 07): Invariant kernels for pattern analysis and machine learning. Machine Learning, 68, 35– 61. SCHÖLKOPF, B. and ... Networks, 12 (5), 987–997. TITSIAS, M.K. and LIKAS, A. (20 02) : Mixtures of Experts Classification Using a Hierarchi- cal Mixture Model. Neural Computation, 14 , 22 21...

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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 5 pdf

Data Analysis Machine Learning and Applications Episode 1 Part 5 pdf

... dendrograms Q 2 0 1 3 4 12 20 32 64 0 f 0022 256 1 0 f 10 0000 3 01f 00 000 4 200f 3422 12 2003f 322 20 2004 3 f 21 32 50 02 2 2 f 5 64 6002 2 1 5 f Fig. 1. 2-adic valuations for D. 0 1 0 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 1 0 64 32 4 20 12 ... random initialization data set COPK-Means ssALife with U*C Atom 71 100 Chainlink 65. 7 10 0 Hepta 10 0 10 0 L...

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Data Analysis Machine Learning and Applications Episode 1 Part 6 docx

Data Analysis Machine Learning and Applications Episode 1 Part 6 docx

... data. grandfather 0.000 0.024 0. 012 0. 965 0.000 grandmother 0.005 0 .13 4 0. 0 16 0.840 0.005 granddaughter 0 .11 3 0.242 0.054 0. 466 0 .12 5 grandson 0 .13 4 0 .11 1 0.052 0.5 81 0 .12 2 brother 0. 61 2 0.282 0.024 0.082 ... 0.000 sister 0.579 0.3 91 0.0 26 0.002 0.002 father 0.099 0.5 46 0 .12 2 0 .15 8 0.075 mother 0.089 0 .65 4 0 .13 6 0.054 0. 066 daughter 0.000 1. 000 0.0...

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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 8 ppsx

Data Analysis Machine Learning and Applications Episode 1 Part 8 ppsx

... case 0 .84 0. 68 0 .82 0 .84 0 .83 0. 68 0.72 0 .88 0. 91 0 .85 0 .85 0. 68 0. 78 0.77 0.72 0 .89 0.66 0.96 0.66 0.93 0.90 0.73 0 .87 0 .88 0 .83 0. 78 0.64 0 .86 0. 78 [ 0.79 0 .89 0. 91 0. 48 0.57 0.49 0.60 0.62 0. 71 0. 71 0.64 0.69 0. 58 0.67 0.66 0.65 0. 61 0.74 0.75 0.72 0.69 0. 58 0.53 0.65 0.45 0.70 0.76 0.75 0.73 umbh tie textiles bag wat mous scul pens 0 ....

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Data Analysis Machine Learning and Applications Episode 1 Part 9 doc

Data Analysis Machine Learning and Applications Episode 1 Part 9 doc

... Ncube, 19 85; Lowry et al., 19 92 ; Jackson, 19 91 ; Liu, 19 95 ; Kourti and MacGregor, 19 96 , Mac- Gregor, 19 97 ). In particular, we focus on the approach based on PLS components proposed by Kourti and ... Gervini and Rousson (2004). At the end, the optimal 202 Rosaria Lombardo, Amalia Vanacore and Jean-Francỗois Durand limits (Wu and Wang, 19 97 ; Jones and Woodal...

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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 1 pot

Data Analysis Machine Learning and Applications Episode 2 Part 1 pot

... 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 11 5 13 9 71 91 44 19 7 12 6 99 33 14 31 17 416 P(N /2) .4 92 .24 3 ... 416 P(N /2) .4 92 .24 3 . 21 4 .14 4 .10 9 .24 4 .17 3 .097 .4 42 .068 .19 0 .8 02 .556 .28 3 R(N /2) . 528 .13 9 .3 02 .19 7 .088 .1 82 .1 52 .19 1...

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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 2...

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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 4 doc

Data Analysis Machine Learning and Applications Episode 2 Part 4 doc

... winners). Misclassification J4.8 J4.8(cv) RPart0 RPart1 QUEST CTree  J4.8 029 911839 J4.8(cv) 40 8911 941 RPart0 560710735 RPart1 641 08 625 QUEST 42 2 50 720 CTree 76789037  26 20 27 38 49 37 Complexity J4.8 J4.8(cv) RPart0 ... 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 0 14 15 81 QUEST 15 13 5 4 0 10 47 CT...

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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

... (4 .5% ) Global mean typical application day cluster 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 ... 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, a...

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