Data Analysis Machine Learning and Applications Episode 2 Part 6 potx

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 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 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 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 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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Data Analysis Machine Learning and Applications Episode 3 Part 6 doc

... storyThe empirical tests on the data from the 66 texts support our hypothesis with good and very good 2 values. Figures 2 and 3 show typical graphs of the theoretical and empirical distributions ... information. Figure 6 shows the relationshipbetween the parameters b and c. Quantitative Text Analysis Using L-, F- and T-Segments 63 9 Table 1. Text numbers in the corpus with respect to genre and authorBrentano ... determinationcoefficient R2, which was above 0.99 in all 66 cases. The parameters b and c of the Distribution of Data in Word Lists 63 1 separation in the past. And again any mathematician has the solution:...

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