Data Analysis Machine Learning and Applications Episode 1 Part 2 potx

Data Analysis Machine Learning and Applications Episode 3 Part 9 docx

... R., 31 9 Bessler, Wolfgang, 499 Biemann, Chris, 577Borgelt, 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 3Fiedler, Mathias, 2 29 Flodman, Pamela, 1 19 Franke, Markus, 35 5Fried, Roland, 277Gabriel, Thomas R., 31 9 Gallo, Michele, 1 93 Gangi, Francesco, ... 127Herrmann, Lutz, 1 39 Hipp, Jochen, 2 53 Holm, Hans J., 6 29 Hornik, Kurt, 147, 38 9, 5 69 Hoser, Bettina, 35 5Hrycej, Tomas, 405Hudec, Marcus, 5 93 Iglesias-Rozas, José R, 55Irpino, Antonio, 7 03 Joaquin...

Data Analysis Machine Learning and Applications Episode 1 Part 1 doc

... 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...

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 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 ...

Data Analysis Machine Learning and Applications Episode 1 Part 3 docx

... 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...

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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 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...

Data Analysis Machine Learning and Applications Episode 1 Part 5 pdf

... 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 Analysis Machine Learning and Applications Episode 1 Part 6 docx

... 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...

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. 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...

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 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 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...

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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 ... 75 ,10 80,30Optimality 97,40 96,60 96 ,10 96 ,10 96 ,10 81, 90 90,40 91, 00 90,50 90,00Note: TCCS, POLCOURT and SCSCIENCE stand for “Teacher, Clerck and CivilServant”, “Politics & Court” and...

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Data Analysis Machine Learning and Applications Episode 3 Part 1 pdf

... 1 035 11 4 15 9 13 17 158 0 . 31 4Polynomial(3rd degree) 12 42 633 516 14 767 17 158 0.6 43 RBF860 6 51 498 15 149 17 158 0.640Coulomb0 11 24 25 16 009 17 158 0 .14 8M1287 850 299 15 722 17 158 0.505M2* 19 1 ... MeNonV BT3Ratios VBT2NonV BT2VBT3NonV BT3Test1 Test2ROS 9.52 3. 93 5 .39 2 .16 1 03 11 6ROE 6.7 3. 83 3 .3 2. 01 111 11 0ROI 6.85 3. 83 -1. 51 1.5 1 13 10 5Leverage 79.75 72.52 226.96 88.28 12 0 58**governance ... 0.505M2* 19 1 33 1 256 12 425 13 2 03 0.655M3** 35 48 33 1 818 12 425 17 158 0.7 43 * 39 55 credit clients could not be classiﬁed.**Default classbadfor 39 55 credit clients. and by w = 11 49 32 3forbadcredit...

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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, 558,871votes 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. The datasets also contain ... and development in information retrieval. New York,NY, USA: ACM Press, 20 02, pp. 2 53 26 0.TSO, K. and SCHMIDT-THIEME L. (20 05): Attribute-aware Collaborative Filtering. In Pro-ceedings of 29 th...

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Data Analysis Machine Learning and Applications Episode 3 Part 3 pps

... WeibullK=2 K =3 K=4 K=5separate 233 39.27 232 02. 23 230 40.01 229 43. 11main.g 233 55.66 230 58.25 22971.86 228 63. 43 main.p 235 03. 73 233 68.77 231 65.60 230 68.47int.gp 235 72.21 234 22.51 233 05. 63 230 75.76main.gp ... 1285 4 4 4 120 120 126 3 2 15 10 13 105 108 107 32 322229497 93 436 30297479795414 233 676564±10 234 3 231 787075 35 9 535 445514248564672 435 195906768182614 30 2595447 433 945 38 4666522252149586651614115 ... jewelleryComponent1 1 .36 234 2 2.981528 1.116042 0.7 935 599 0.91454 63 Component2 1 .36 234 2 2.981528 1.116042 0.7 935 599 0.91454 63 Component3 1 .36 234 2 2.981528 1.116042 0.7 935 599 0.91454 63 Component4 1 .36 234 2 2.981528...

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Data Analysis Machine Learning and Applications Episode 3 Part 4 potx

... Equation 3 and Figure 4: Pnìm= UnìcÃScìcÃVcìm (3) 2.69 0.57 2.22 4. 250.78 3. 93 2.21 0. 04 3. 17 1 .38 2.92 4. 78Pnìi-0.61 0.28-0.29 -0.95-0. 74 0. 14 Unìc8.87 00 4. 01Scìc-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 0.18Portal 3 0.0058 0. 245 5 0.02 54 0. 245 9 0 .33 82 0 .41 75 0.15Fig. 2. Decision matrix ... f1does.f1f2f 3 f 4 U1 4 1 1 4 U21 4 2 0U 3 2 1 4 5(a)f1f2f 3 f 4 U 4 1 4 1 0(b)Fig. 2. User-Feature matrix P divided in (a) Training Set (nìm), (b) Test Set 3. 2 Applying SVD on training data Initially,...

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