Data Analysis Machine Learning and Applications Episode 2 Part 5 pps

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 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 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 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 ...

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):LMAO=[ûW 2 û/ˆV 2 ] 2 T 22 −(T 21 A) 2 ˆvar(Û), (6)LMAU=[ûBBW1y] 2 Hrho−HTUˆvar(ˆT)HTU, (7)where T 21 A= tr[W 2 W1A−1+W 2 W1A−1], A = I −ÛW1, ... sequences.Intelligent Data Analysis, 6(3) :23 7 25 5.KAM, P S. and FU, A. W C. (20 00): Discovering Temporal Patterns for Interval-BasedEvents. In: Data Warehousing and Knowledge Discovery, 2nd Int. Conf., DaWaK 20 00.Springer,...

Data Analysis Machine Learning and Applications Episode 2 Part 3 pps

... preparation (data= d1, variable='lname',method='asoundex') lname asoundex.lname11 525 6 WESTERHEIDE W 236 20 0001 BESTEWEIDE B 233 20 00 02 WESTERWELLE W 236 3. 3 Candidate selectioncandidates (data1 , ... retains only 83 candidates.> candidates (data1 =d1.prep, data2 =d2.prep,method='blocking',selvars1='asoundex.lname')> candidates (data1 =d1.prep, data2 =d2.prep,method='sorted', ... U=0.5W 2 W 2 , O=0.5W 2 W 2 , U=0.50 0.05 0.1 0.15 0 .2 00.10 .2 0 .3 0.40.50.60.70.80.91U,Opowerb) SARAR(1,1): GMM opt.inst. WaldW1W1, O=0.5W1W1, U=0.5W 2 W 2 , O=0.5W 2 W 2 ,...

Data Analysis Machine Learning and Applications Episode 2 Part 4 doc

... winners).MisclassiﬁcationJ4.8 J4.8(cv) RPart0 RPart1 QUEST CTreeJ4.8 029 911839J4.8(cv) 40 8911 941 RPart0560710735RPart1 641 08 625 QUEST 42 2 50 720 CTree76789037 26 20 27 38 49 37Complexity J4.8 J4.8(cv) RPart0 ... RPart0 RPart1 QUEST CTreeJ4.8 010 020 3J4.8(cv)17 0 0 0 5 3 25 RPart018 18 0 0 13 15 64 RPart118 18 16 0 14 15 81QUEST15 13 5 4 0 10 47 CTree18 14 3 2 8 0 45 86 64 24 6 42 43 Table ... Ŧ RPart1QUEST Ŧ RPart1CTree Ŧ RPart0QUEST Ŧ RPart0RPart1 Ŧ RPart0CTree Ŧ J4.8(cv)QUEST Ŧ J4.8(cv)RPart1 Ŧ J4.8(cv)RPart0 Ŧ J4.8(cv)CTree Ŧ J4.8QUEST Ŧ J4.8RPart1 Ŧ J4.8RPart0 Ŧ J4.8J4.8(cv)...

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 6 potx

... 361 108 560 1 8 26 109 36 0 26 28518318 16 923 1 021 511948 123 47 02 1 924 30044079003 5 26 981858 24 7198 924 001898 26 3 26 62 8 2000051451775 22 800903 921 91 060 100 56 1 922 70 460 01 969 9 761 860 18471 8 26 18 161 9481 924 1858189817751 922 1 969 1847108 560 1 8 26 109 36 0 26 28518318 16 923 1 021 511948 123 47 02 1 924 30044079003 5 26 981858 24 7198 924 001898 26 3 26 62 8 2000051451775 22 800903 921 91 060 100 56 1 922 70 460 01 969 9 761 860 18471 8 26 18 161 9481 924 1858189817751 922 1 969 1847Fig. ... accuracy (67 %) and while some cover a wide Analysis of Stock Markets 361 108 560 1 8 26 109 36 0 26 28518318 16 923 1 021 511948 123 47 02 1 924 30044079003 5 26 981858 24 7198 924 001898 26 3 26 62 8 2000051451775 22 800903 921 91 060 100 56 1 922 70 460 01 969 9 761 860 18471 8 26 18 161 9481 924 1858189817751 922 1 969 1847108 560 1 8 26 109 36 0 26 28518318 16 923 1 021 511948 123 47 02 1 924 30044079003 5 26 981858 24 7198 924 001898 26 3 26 62 8 2000051451775 22 800903 921 91 060 100 56 1 922 70 460 01 969 9 761 860 18471 8 26 18 161 9481 924 1858189817751 922 1 969 1847Fig. ... 361 108 560 1 8 26 109 36 0 26 28518318 16 923 1 021 511948 123 47 02 1 924 30044079003 5 26 981858 24 7198 924 001898 26 3 26 62 8 2000051451775 22 800903 921 91 060 100 56 1 922 70 460 01 969 9 761 860 18471 8 26 18 161 9481 924 1858189817751 922 1 969 1847108 560 1 8 26 109 36 0 26 28518318 16 923 1 021 511948 123 47 02 1 924 30044079003 5 26 981858 24 7198 924 001898 26 3 26 62 8 2000051451775 22 800903 921 91 060 100 56 1 922 70 460 01 969 9 761 860 18471 8 26 18 161 9481 924 1858189817751 922 1 969 1847Fig. 2. Reduced adjacency matrix entries for...

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Data Analysis Machine Learning and Applications Episode 2 Part 7 docx

... Identiﬁcation. Socimetry, 28 , 27 7 29 9.OKADA, A. (20 03): Using Additive Conjoint Measurement in Analysis of Social Network Data. In: M. Schwaiger, and O. Opitz (Eds.): Exploratory Data Analysis in EmpiricalResearch. ... 2 Characteristic valuesActor (Family) 4 .23 3 3.4181 Acciaiuoli 0. 129 0.134 2 Albizzi 0 .21 0 0.3003 Barbadori 0. 179 0.0534 Bischeri 0. 328 -0 .26 05 Castellani 0 .29 6 -0.3536 Ginori 0.094 0. 123 7 ... between subgroups 1 and 2. 0-0.5-0.4-0.3-0 .2 -0.10.10 .2 0.30.40.5-0.5 -0.4 -0.3 -0 .2 -0.1 0.1 0 .2 0.3 0.4 0.5 2 Albizzi3 Barbadori4 Bischeri5 Castellani6 Ginori 7 Guadagni8 Lamberteschi9...

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Data Analysis Machine Learning and Applications Episode 2 Part 8 docx

... 7. 78 24 (2) .24 (1) 12. 84 32 (2) . 32 (1)Type of building 9.09 . 08 (2) 22 (3) 8. 36 03 (2) 12 (3).14 (1) .15 (1)Outside facilities 7.40 .25 (1) .00 (2) 12. 11 . 28 (1) 09 (2) 25 (3) 19 (3)(* The ... (1) 37 (3) 26 (3)Beach 9 .83 10 (2) .35 (1) 5.56 09 (2) .26 (1) 25 (3) 17 (3)Hotel servicesLeisure activities 11. 72 20 (6) 02 (2) 7. 52 04 (6) . 02 (2) .04 (2) .20 (1) 01 (4) .01 (3).01 (3) ... (4) 12. 49 . 08 (1) 09 (4) 03 (3) .06 (2) 01 (3) .03 (2) Catering 12. 17 19 (5) .03 (3) 13 .29 07 (5) 01 (3). 12 (1) 07 (4) . 02 (2) 04 (4).10 (2) .10 (1)Hotel facilitiesLocation 7. 78 24 (2) .24 ...

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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 2 Part 10 docx

... ’000) 20 00 20 01 20 02 2003 20 040 25 5075 100 125 150Box Jenkins (# 10) SE (in ’000) 20 00 20 01 20 02 2003 20 040 25 5075 100 125 150Linear Regression (# 2) SE (in ’000) 20 00 20 01 20 02 2003 20 040 25 5075 100 125 150VAR(4)-Model ... 0.00.51.01. 52. 02. 53.0ARL 3 42. 18 341. 42 339. 42 334. 52 326 .63 316.80 306. 92 SDRL 338.74 338. 62 338.77 338.89 338.54 337.80 337.35 10 Q( .10) 38 37 35 30 22 12 5Q(.50) 23 8 23 7 23 6 23 0 22 2 21 2 20 1Q(.90) ... 20 040 25 5075 100 125 150VAR(4)-Model (# 17)SE (in ’000) 20 00 20 01 20 02 2003 20 040 25 5075 100 125 150BVAR(18)-Model (# 23 )SE (in ’000) 20 00 20 01 20 02 2003 20 040 25 5075 100 125 150Fig. 2. ...

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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 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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