Data Analysis Machine Learning and Applications Episode 1 Part 3 docx

... able2 14 4423284446224389 11 20 16 15 6 21 50 13 302749 1 5292834735223 31 3748 12 2639 10 45 17 23257598 18 433633 19 47907082 71 41 40577894845888795955 51 91 738564 61 65628096898395 10 06354749253728776939766 81 696799 11 3688656607752 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 0.20.30.40.50.60.70.80.9 1 (b)Fig. ... able1 14 4423284446224389 11 20 16 15 6 21 50 13 302749 1 5292834735223 31 3748 12 2639 10 45 17 23257598 18 433633 19 47907082 71 41 40577894845888795955 51 91 738564 61 65628096898395 10 06354749253728776939766 81 696799 11 3688656607752 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. ... able12435 18 41 62633830 61 54 10 07660996255 71 22 14 27 12 204677926463897378 17 39 16 11 21 35044678666598532344938 31 9795986587885393562836 19 10 45525968490 1 42294474879 51 57 81 91 759440 15 7 11 27468828369728029374352 19 2 13 23 11 4 11 0 11 5 17 0 15 65870 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 0n0 .1 0.20.30.40.50.60.70.80.9(a)Vari...

... 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 22 44.TUTZ, G. and BINDER H. (20 05): ... 0. 619 P 1 v–rest,no0.973 0. 618 0.803 0.646 0.9 81 0.588P 1 v–rest,map0.973 0.9 42 0.803 0.785 0.978 0.9 21 P 1 v–rest,assign0.973 0.896 0.796 0.7 52 0.976 0. 829 P 1 v–rest,Dirichlet0.973 0.963 0. 815 ...

... 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 . 23 Classif. Accuracy % 95.78 % 91. 01 % 91. 01 % 1. 05 1. 05USPS RBF H1-SVM H1-SVM RBF/H1 RBF/H1(Min-Max) Kernel ... 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. 33 3 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 ... grade 1 tumors were classified as grade 3 in 2.26%of the cases.4 0.00 0.00 4.20 48 .33 3 1. 92 8. 31 70 .18 49 .17 2 26. 83 79.80 22.26 0.00 1 71. 25 11 .89 3. 35 2.50LVQ 1 2 3 44 0.00 0.28 2 .10 50. 83 3...

... 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 30ccr 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 8b 0. 910 71 0. 848 88 0 .48 550 0.73720 0. 813 17 0.79 644 0.72899 0. 744 626a 0. 610 74 0.608 21 0 .13 40 0 0.53296 0. 610 37 0.5 642 6 0.3 511 3 0 .47 885b ... 0.85 946 0.60606 0.3 612 1 0. 610 90 0.68223 0. 5 14 87 0 .4 919 9 0. 611 56 4 a 0.35609 0 .44 997 0.0 012 7 0 .43 860 0.53509 0 .47 083 0. 046 77 0.00295b 0.83993 0.872 24 0.56 313 0.565 41 0.8 0 14 9 0.6 210 2 0.5 41 0 9 0.8 015 65a...

... dendrogramsQ20 1 3 4 12 20 32 640 f 0022 256 1 0 f 10 0000301f 00 0004200f 3422 12 2003f 322202004 3 f 21 32 50 02 2 2 f 5 646002 2 1 5 fFig. 1. 2-adic valuations for D.0 1 0 1 0 1 20 1 30 1 40 1 5 0 1 60 1 06432420 12 ... random initialization data set COPK-Means ssALife with U*CAtom 71 100Chainlink 65. 7 10 0Hepta 10 0 10 0Lsun 96.4 10 0Target 55 .2 10 0Tetra 10 0 10 0TwoDiamonds 10 0 10 0Wingnut 93.4 10 0EngyTime 90 ... ensembleGordon and Vichi (20 01, Table 1) provide soft partitions of 21 countries based onmacroeconomic data for the years 19 75, 19 80, 19 85, 19 90, and 19 95. These parti-tions were obtained using fuzzy c-means...

... data. grandfather 0.000 0.024 0. 012 0. 965 0.000grandmother 0.005 0 .13 4 0. 0 16 0.840 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. 61 2 0.282 0.024 0.082 ... 0.000sister0.579 0.3 91 0.0 26 0.002 0.002father 0.0990.5 46 0 .12 2 0 .15 8 0.075mother 0.0890 .65 4 0 .13 6 0.054 0. 066 daughter 0.000 1. 000 0.000 0.000 0.000son 0.0 31 0.842 0.007 0 .11 3 0.007nephew 0. 012 0.047 ... Name) (Product Name) (Price)(x 1 ,x2) 0 .6 1 0.0 76 (0 .6, 1, 0.0 76) 0.8(x 1 ,x3) 0 .1 0 0.849 (0 .1, 0, 0.849) 0.2(x2,x3) 0.0 0 0. 860 (0.0, 0, 0. 860 ) 0 .1 4 .1 Collective decision model with...

... 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. 975 ˆT 1/ 2,where the degrees of freedom are Q =(m 1) {1 +[ (1 + m 1 )ˆB] 1 ˆU}, (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...

... 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...

... 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 ... Gervini and Rousson (2004). At the end, the optimal 202 Rosaria Lombardo, Amalia Vanacore and Jean-Francỗois Durandlimits (Wu and Wang, 19 97 ; Jones and Woodall, 19 98 ; Liu and Tang, 19 96 ) has ... 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...