Data Analysis Machine Learning and Applications Episode 1 Part 5 pdf

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 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 3R(N /2) . 528 .13 9 .3 02 .19 7 .088 .1 82 .1 52 .19 1 .26 3 .0 61 .14 3 . 710 .3 52 .3 61 29 6 Triebel et al. data point p whose ... Situations 27 5 -16 00 -14 00- 12 0 0 -10 00-800-600-400 -20 0 0 5 10 15 20 25 30log likelihoodtime (s)passingaborted passingfollow -20 0 -10 0 0 10 0 20 0 300 400 500 600 4 6 8 10 12 14 16 18 20 ...

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Data Analysis Machine Learning and Applications Episode 2 Part 5 pps

... (4 .5% )Global meantypical application day cluster numberunknownupunknowndownp2pupp2pdownwebupwebdown0 5 10 15 20 25 01 2 34 5 6x 1060 5 101 5 2 0 2 5 00 .5 11 .5 2 2 .5 x ... 20 30 40 50 60 70 8000 .2 0.40.60.81Typical day 12 0 5 10 15 20 25 01 2 34 5 x 1070 5 101 5 2 0 2 5 0 2 46810x 106cluster 6, application: p2p down ( 12% ) volume (in byte)global ... technical comments and Jörg Fenner for helping collectthe raw and context information and evaluate the text mining approach. 350 Francoise Fessant et al.0 5 10 15 20 25 01 2 34 5 6x 106hoursvolume...

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Data Analysis Machine Learning and Applications Episode 2 Part 9 pdf

... pseudo R 2 1 -131. 49 28 0 .97 28 9. 97 .00 .23 2 -117.04 27 6. 09 29 7. 09 . 09 .813 -100 .96 26 7. 92 300. 92 .08 . 92 4 - 89. 76 26 9. 52 314. 52 .11 . 92 5 - 82. 62 2 79 .24 336 .24 .11 .95 Classifying Contemporary ... biasedT04s8 .93 2. 10 2. 59 biasedT05s10. 59 -8.75 -4.70 biasedTM score 3.67 4.05 .88 10 . 29 DM score 2. 71 1.03 -7.87 8 .94 EM score -2. 44 64 1.37 6. 62 IM score 1.15 .23 6. 52 5.17NM score 44 . 29 2. 99 3.03Intercept ... 0.0 32 0.044 0. 020 Level 2 18 .2 % 0.117 14.4 % 0.080 21 .8 % 0.154Level 3 0.140 0. 095 0.184Attribute 2 Level 1 0.106 0.177 0.036Level 2 0.157 0.183 0.1 32 Level 3 27 .5 %0 .23 8 28 .5 %0 .23 6 26 .5...

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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 5 pdf

... weight-ings.Rand cRandktf tf-idf tf tf-idf 3 0.48 0.49 0. 03 0. 03 40 .51 0 .52 0. 03 0. 03 5 0 .54 0. 53 0.02 0.0260 .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 0Percentage0.000 25. 654 74 .34 6 0.000Human Percentage2.116 27 .30 6 70 .55 1 0.027Jurisdictions of the Austrian supreme ... shifts:ã shift 0: Extrablatt 0 T1 15 O 53 1 T2 15 house Hauptstr 2 T64street Heidelberg 3 T 15 city 69117 4 T2 15 zipã shift 1: 1 -1:Extrablatt -1:T1 15 0: 53 0:T2 15 1:Hauptstr 1:T64house Projecting...

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