Data Analysis Machine Learning and Applications Episode 3 Part 8 doc
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...
- 3
- 339
- 0
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...
- 25
- 341
- 0
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...
- 25
- 377
- 0
Data Analysis Machine Learning and Applications Episode 1 Part 7 doc
... follows:E(t +1) |···∼W2G +2gD,(2h+ 2Kk =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...
- 25
- 358
- 0
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 WatchLeatherTrayleatherWatchLeatherKerchief2TŦshirtŦVCapTrayleather0.75[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...
- 25
- 475
- 0
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...
- 25
- 315
- 0
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 ...
- 25
- 237
- 0
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...
- 25
- 318
- 0
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...
- 25
- 314
- 0
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...
- 25
- 356
- 0
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,...
- 25
- 412
- 0
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...
- 25
- 209
- 0
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:...
- 25
- 247
- 0
Data Analysis Machine Learning and Applications Episode 3 Part 7 ppt
... Measurements and the corresponding ranks of MnOValue 0.01 0.02 0. 03 0.04 0.05 0.06 0. 07 0.08 0.09 0.10 0.11 0. 13 Frequency 171 82 071 54 231 11Rank 9 26.5 45.5 59 63 66 70 .5 73 . 5 76 78 79 80Transformation ... <T1– 07& gt;-0 .70 7-Geographic treatment <T1– 070 7> 7- North America <na4r7span:T1– 070 1-T1– 070 9:T2 7& gt; 73 United States <na4r7span:T1– 070 1-T1– 070 9:T2– 73 & gt;The information given in angle ... our DDC analysis diagram for the 37 th molecular DDC notationof his sample:Liu (19 93) , pp. 99–100 72 0 .70 73 has been decomposed as follows: 72 0: Architecture 070 7: Geographical treatment 73 : United...
- 25
- 263
- 0
Data Analysis Machine Learning and Applications Episode 3 Part 8 doc
... Linguistics, 637 , 655Question Answering, 5 53 R, 33 5, 38 9, 569Rank Data, 681 Recommender Systems, 525, 533 , 541,619Record Linkage, 33 5Reference Modelling, 37 3Regression, 36 3Relationships, 36 3, 629Return ... Segmentation, 479 Data Analysis, 31 9 Data Augmentation, 111 Data Depth, 455 Data Integration, 33 5 Data Mining, 421 Data Quality, 33 5 Data Transformation, 681 Decision Trees, 38 9Dendrograms, 95Design ... Machines, 3, 11, 55,77, 245, 515Supreme Administrative Court, 569Survival Analysis, 5 93 Swarm Intelligence, 139 Tagged Data, 6 73 Taxonomies, 37 3Temporal Data Mining, 2 53 Text Analysis, 637 Text...
- 25
- 301
- 0
Từ khóa: data mining techniques tools and applicationsdata mining tasks techniques and applicationsdata link layer protocols and applicationsapplied microarray data analysis using r and sas softwarebasic machine learning and object detection based on keypointsmachine learning and statistical techniqueschapter 4 data analysis with hive and pig in amazon emrdata analysis research findings and discussiondata analysis major findings and suggestionsfinancial management theory and practice brigham11th ed chapter 8 doctiếng nhật dành cho người mới bắt đầu tập 3 part 8 pdfdata mining practical machine learning tools and techniques amazondata mining practical machine learning tools and techniques pdf downloaddifferentiation between benign and malignant skin tumors by image analysis neural networks and other methods of machine learningcomplex multi data type and multi tool analysis principles and applications of using workflow and pipelining methodsNghiên cứu sự biến đổi một số cytokin ở bệnh nhân xơ cứng bì hệ thốngNghiên cứu tổ chức pha chế, đánh giá chất lượng thuốc tiêm truyền trong điều kiện dã ngoạiNghiên cứu tổ chức chạy tàu hàng cố định theo thời gian trên đường sắt việt namđề thi thử THPTQG 2019 toán THPT chuyên thái bình lần 2 có lời giảiGiáo án Sinh học 11 bài 13: Thực hành phát hiện diệp lục và carôtenôitGiáo án Sinh học 11 bài 13: Thực hành phát hiện diệp lục và carôtenôitGiáo án Sinh học 11 bài 13: Thực hành phát hiện diệp lục và carôtenôitGiáo án Sinh học 11 bài 13: Thực hành phát hiện diệp lục và carôtenôitGiáo án Sinh học 11 bài 13: Thực hành phát hiện diệp lục và carôtenôitPhối hợp giữa phòng văn hóa và thông tin với phòng giáo dục và đào tạo trong việc tuyên truyền, giáo dục, vận động xây dựng nông thôn mới huyện thanh thủy, tỉnh phú thọNghiên cứu, xây dựng phần mềm smartscan và ứng dụng trong bảo vệ mạng máy tính chuyên dùngNghiên cứu tổng hợp các oxit hỗn hợp kích thƣớc nanomet ce 0 75 zr0 25o2 , ce 0 5 zr0 5o2 và khảo sát hoạt tính quang xúc tác của chúngTìm hiểu công cụ đánh giá hệ thống đảm bảo an toàn hệ thống thông tinThiết kế và chế tạo mô hình biến tần (inverter) cho máy điều hòa không khíChuong 2 nhận dạng rui roGiáo án Sinh học 11 bài 14: Thực hành phát hiện hô hấp ở thực vậtGiáo án Sinh học 11 bài 14: Thực hành phát hiện hô hấp ở thực vậtChiến lược marketing tại ngân hàng Agribank chi nhánh Sài Gòn từ 2013-2015Đổi mới quản lý tài chính trong hoạt động khoa học xã hội trường hợp viện hàn lâm khoa học xã hội việt namHIỆU QUẢ CỦA MÔ HÌNH XỬ LÝ BÙN HOẠT TÍNH BẰNG KIỀM