Data Analysis Machine Learning and Applications Episode 1 Part 8 ppsx

... case0 .84 0. 68 0 .82 0 .84 0 .83 0. 68 0.720 .88 0. 91 0 .85 0 .85 0. 68 0. 78 0.770.720 .89 0.660.960.660.930.900.730 .87 0 .88 0 .83 0. 78 0.640 .86 0. 78 [0.790 .89 0. 91 0. 48 0.570.490.600.620. 71 0. 71 0.640.690. 58 0.670.660.650. 61 0.740.750.720.690. 58 0.530.650.450.700.760.750.73umbhtietextilesbagwatmoussculpens0 .85 WatchLeatherTrayleatherWatchLeatherKerchief2TŦshirtŦVCapTrayleather0.75[Fig. ... respondents.E([)=0. 086 5 ∗umbh+ 0 .13 35∗tie+ 0.20 41 textiles+ 0. 211 4∗bag+0 .17 91 wat + 0 .12 92∗mous+ 0. 08 81 scul+ 0.2322∗pens (1) 13 8 Christian Hennig and Pietro CorettoCAMPBELL, N. A. (19 84 ): Mixture models and ... 5 10 1520250.00 0.05 0 .10 0 .15 Two outliersxDensity0 5 10 15 200.00 0.05 0 .10 0 .15 Wide noisexDensityŦ5 0 5 10 1520250.00 0.02 0.04 0.06 0. 08 0 .10 Noise on one sidexDensityŦ5 0 5 10 1520250.00...

Data Analysis Machine Learning and Applications Episode 1 Part 1 doc

... able1 14 44232 8 4446224 38 9 11 20 16 15 6 21 50 13 302749 1 529 28 34735223 31 37 48 12 2639 10 45 17 232575 98 18 433633 19 479070 82 71 41 4057 78 94 84 58 88 795955 51 91 73 85 64 61 6562 80 96 89 83 95 10 0635474925372 87 76939766 81 696799 11 3 68 8656607752 13 9 13 4 13 0 10 3 13 8 10 9 14 0 14 3 11 4 12 4 13 7 12 7 12 6 13 3 10 7 10 4 13 1 14 6 10 8 13 5 14 4 11 1 11 7 13 6 10 5 14 2 13 2 11 5 12 1 14 7 15 0 10 1 14 5 11 0 12 2 12 5 10 2 14 1 10 6 14 8 12 0 11 6 11 9 12 9 12 8 15 5 11 2 11 8 12 3 14 9 17 2 17 7 16 9 16 7 15 9 17 8 16 4 19 8 16 0 15 7 18 4 16 3 17 6 19 9 17 1 18 2 16 2 19 5 15 8 19 6 15 2 17 0 18 1 16 6 18 9 15 3 18 6 17 5 19 7 19 0 17 9 19 2 15 6 16 5 19 1 18 5 18 8 19 4 17 4 15 1 15 4 18 0200 16 8 17 3 18 7 16 1 19 3 18 3n00. ... able2 14 44232 8 4446224 38 9 11 20 16 15 6 21 50 13 302749 1 529 28 34735223 31 37 48 12 2639 10 45 17 232575 98 18 433633 19 479070 82 71 41 4057 78 94 84 58 88 795955 51 91 73 85 64 61 6562 80 96 89 83 95 10 0635474925372 87 76939766 81 696799 11 3 68 8656607752 13 9 13 4 13 0 10 3 13 8 10 9 14 0 14 3 11 4 12 4 13 7 12 7 12 6 13 3 10 7 10 4 13 1 14 6 10 8 13 5 14 4 11 1 11 7 13 6 10 5 14 2 13 2 11 5 12 1 14 7 15 0 10 1 14 5 11 0 12 2 12 5 10 2 14 1 10 6 14 8 12 0 11 6 11 9 12 9 12 8 15 5 11 2 11 8 12 3 14 9 17 2 17 7 16 9 16 7 15 9 17 8 16 4 19 8 16 0 15 7 18 4 16 3 17 6 19 9 17 1 18 2 16 2 19 5 15 8 19 6 15 2 17 0 18 1 16 6 18 9 15 3 18 6 17 5 19 7 19 0 17 9 19 2 15 6 16 5 19 1 18 5 18 8 19 4 17 4 15 1 15 4 18 0200 16 8 17 3 18 7 16 1 19 3 18 3n00 .1 0.20.30.40.50.60.70 .8 0.9 1 (b)Fig. ... ix2435 18 41 62633 8 30 61 54 10 07660996255 71 22 14 27 12 204677926463 89 73 78 17 39 16 11 21 3504467 86 6659 85 323449 38 31 9795 98 65 87 88 539356 28 36 19 10 4552596 84 90 1 4229447 48 79 51 57 81 91 759440 15 7 11 274 68 82 83 6972 80 29374352 19 2 13 23 11 4 11 0 11 5 17 0 15 6 58 70 15 2 19 0 11 1 14 8 12 8 15 9 15 1 16 9 18 4 18 6 12 4 10 6 11 7 13 3 11 9 10 2 14 2 12 3 14 1 19 4 15 5 17 8 12 0 13 6 10 5 11 8 16 2 16 7 19 6 17 2 18 7 16 5 15 7 16 4 10 4 14 6 11 3 13 5 12 7 15 3 19 8 16 8 17 3 18 5 16 3 18 8 17 6 18 3 19 5 17 1 14 4 13 1 15 0 10 3 14 7 14 9 12 9 17 5 19 3200 17 7 18 2 12 2 11 6 10 7 12 6 13 0 13 8 16 0 17 9 18 9 15 4 10 1 10 8 14 0 19 7 16 6 17 4 12 5 14 5 12 1 13 7 14 3 10 9 13 4 13 2 13 9 15 8 19 9 18 1 16 1 19 1 18 000.3 68 0.737Fig....

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Data Analysis Machine Learning and Applications Episode 1 Part 2 potx

... 0.7 61 0.694 0.677 0 .88 6 0 .83 2Pall–pairs,Dirichlet0 .89 3 0.755 0.720 0. 688 0 .88 8 0.7 71 P 1 v–rest,no0 .83 3 0.539 0. 688 0.570 0 .88 5 0.464P 1 v–rest,map0 .87 3 0.647 0. 682 0.563 0 .87 8 0. 784 P 1 v–rest,assign0 .86 7 ... 0. 619 P 1 v–rest,no0.973 0. 6 18 0 .80 3 0.646 0.9 81 0. 588 P 1 v–rest,map0.973 0.942 0 .80 3 0. 785 0.9 78 0.9 21 P 1 v–rest,assign0.973 0 .89 6 0.796 0.752 0.976 0 .82 9P 1 v–rest,Dirichlet0.973 0.963 0. 81 5 ... 0 .86 2 0 .83 5PQDA0. 980 0.969 0.7 71 0.7 61 0. 914 0 .86 6PLogistic Regression0.973 0.964 0.5 61 0.633 0 .84 3 0.572Ptree0.927 0 .8 21 0.427 0.556 0.746 0.664PNaive Bayes0.947 0.936 0.650 0.668...

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Data Analysis Machine Learning and Applications Episode 1 Part 3 docx

... 40 60 80 10 0 12 0 14 0 16 0 18 0 2000204060 80 10 0 12 0 14 0 16 0 18 02000 20 40 60 80 10 0 12 0 14 0 16 0 18 0 2000204060 80 10 0 12 0 14 0 16 0 18 0200Fig. 2. Problem fourclass (Schoelkopf and Smola ... 2.62 3 .87 77.30 46.672 28. 83 88 . 41 18. 06 2.50 1 68. 54 7.44 2.54 0.00SRNG 1 2 3 44 0.00 0.56 2. 08 53.333 0.67 3.60 81 . 12 44 .17 2 28. 21 85 .35 15 .54 2.50 1 71. 12 10 .50 1. 25 0.00SVM 1 2 3 4Total ... 14 :55.23 10 :55.70 14 : 21. 99 1. 37 1. 04Classification Time 03 :13 .60 00 :14 .73 00 :14 .63 13 .14 13 .23Classif. Accuracy % 95. 78 % 91. 01 % 91. 01 % 1. 05 1. 05USPS RBF H1-SVM H1-SVM RBF/H1 RBF/H1(Min-Max) Kernel...

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Data Analysis Machine Learning and Applications Episode 1 Part 4 pptx

... 0.3 511 3 0.4 788 5b 0 .83 880 0 .8 7 18 3 0.56074 0.75 584 0 .86 282 0. 81 3 95 0. 710 85 0.79 0 18 7a 0 .10 84 8 0 .11 946 0.00 517 0.09267 0 .10 945 0 .11 88 3 0.00 389 0.00659b 0 .80 072 0 .87 399 0.27965 0 .87 892 0.9 488 2 ... centroid2a 0. 380 47 0.53576 0.00022 0 .11 912 0.42 288 0.2 511 4 0.00527 0.00032b 0 .84 2 18 0.90705 0.72206 0 .12 010 0.99 680 0. 417 96 0.304 51 0 .89 8353a 0.276 81 0.340 71 0.00 288 0.29392 0.40 81 8 0.35435 0.04625 ... 0.5 410 9 0 .8 015 65a 0.54746 0.6 013 9 0.27 610 0.46735 0. 580 50 0.4 984 2 0.33303 0.5 017 8 b 0. 910 71 0 .84 888 0. 485 50 0.73720 0. 81 3 17 0.79644 0.7 289 9 0.744626a 0. 610 74 0.6 08 21 0 .13 400 0.53296 0. 610 37...

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Data Analysis Machine Learning and Applications Episode 1 Part 5 pdf

... dendrogramsQ20 1 3 4 12 20 32 640 f 0022256 1 0 f 10 0000301f 00 0004200f 3422 12 2003f 322202004 3 f 21 325002 2 2 f 5646002 2 1 5 fFig. 1. 2-adic valuations for D.0 1 0 1 0 1 20 1 30 1 40 1 50 1 60 1 06432420 12 ... 0.0 08 Bolivia0.666 0.056 0.2 78 Canada 0. 0 18 0. 980 0.002Chile0.632 0.356 0. 012 Egypt0.750 0.070 0 . 18 0France 0. 012 0. 988 0.000Greece0.736 0 .19 4 0.070India0.542 0.076 0. 382 Indonesia0. 616 ... 0.006U.K. 0 .11 20 .87 2 0. 016 U.S.A. 0.0620.930 0.0 08 Uruguay0. 680 0. 310 0. 010 Venezuela0.600 0.390 0. 010 3 Applications 3 .1 Gordon-Vichi macroeconomic ensembleGordon and Vichi (20 01, Table 1) provide...

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Data Analysis Machine Learning and Applications Episode 1 Part 6 docx

... 0.000 0.024 0. 012 0.965 0.000grandmother 0.005 0 .13 4 0. 016 0 .84 0 0.005granddaughter 0 .11 3 0.242 0.0540.466 0 .12 5grandson 0 .13 4 0 .11 1 0.0520.5 81 0 .12 2brother0. 612 0. 282 0.024 0. 082 0.000sister0.579 ... Name) (Product Name) (Price)(x 1 ,x2) 0.6 1 0.076 (0.6, 1, 0.076) 0 .8 (x 1 ,x3) 0 .1 0 0 .84 9 (0 .1, 0, 0 .84 9) 0.2(x2,x3) 0.0 0 0 .86 0 (0.0, 0, 0 .86 0) 0 .1 4 .1 Collective decision model with ... 0.000sister0.579 0.3 91 0.026 0.002 0.002father 0.0990.546 0 .12 2 0 .15 8 0.075mother 0. 089 0.654 0 .13 6 0.054 0.066daughter 0.000 1. 000 0.000 0.000 0.000son 0.0 31 0 .84 2 0.007 0 .11 3 0.007nephew 0. 012 0.047...

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Data Analysis Machine Learning and Applications Episode 1 Part 7 doc

... follows:E(t +1) |···∼W2G +2gD,(2h+ 2Kk =1 6(t) 1 k) 1 ,S(t +1) |···∼D(J+ n 1 , ,J +nK),z(t +1) k|···∼N(nk6(t) 1 k+ <) 1 (nk6(t) 1 kyk+ <[),(nk6(t) 1 k+ <) 1 ,6 1( t +1) k|···∼W⎛⎝2D ... + (1+ m 1 )ˆB, and a 95% confidence interval for Q is given byˆQ±tQ,0.975ˆT 1/ 2,where the degrees of freedom are Q =(m 1) {1 +[ (1 + m 1 )ˆB] 1 Û}, (see Rubin, 19 87 ). 10 4 Daniela G. ... -0. 014 4 0 .13 23 93.7% 0.5000M20.0 014 0 .13 16 94.9% 0. 516 3Table 2. Results of the experiment where population is based on Multivariate GammaMod bias MSE S.Cov LengthNM 0.0 015 0.04 31 93 .8% 0 .16 04M20.0052...

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Data Analysis Machine Learning and Applications Episode 1 Part 9 doc

... La Revue de Modulad, 31, 1 31. D’ AMBRA, L. and LAURO, N. (19 89 ): Non symetrical analysis of three-way contingencytables. Multiway Data Analysis, 3 01 315 .ESCOFIER, B. (19 83 ): Généralisation de ... B0= B and Y0= Y the design and response data matrices, respec-tively. Define t 1 = B0w 1 and u 1 = Y0c 1 as the first MAPLSS components, wherethe weighting unit vectors w 1 and c 1 are ... 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 componentsproposed by Kourti and...

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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 instrumentnotes flu guit pian trum violc4–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 0c4–a4 1 1 1 0 0c4–c5 1 1 1 1 1 instrumentnotes flu guit pian trum violc4–c4 1 1 1 1 1 c4–e4 0 1 0 1 1c4–g4 1 1 1 1 1 c4–a4 1 1 1 0 0c4–c5 1 1 1 1 ... 64,40 75,30 83 ,00 89 ,20 43,70 64,40 75,30 83 ,00 89 ,20Simple solu-tion42,50 62,20 72,30 79,70 85 ,70 35 ,80 58, 20 68, 50 75 ,10 80 ,30Optimality 97,40 96,60 96 ,10 96 ,10 96 ,10 81 , 90 90,40 91, 00 90,50...

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