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Data Analysis Machine Learning and Applications Episode 3 Part 9 docx
... Thiel, Klaus, 4 79 Mair, Patrick, 5 93 Manni, Franz, 645 March, Nicolas, 4 39 Marinho, Leandro B., 533 Mehler, Alexander, 6 53 Meinl, Thorsten, 31 9 Meißner, Martin, 447 Merkel, Andreas, 5 53 Messaoud, ... Martin, 39 7 Schiffner, Julia, 69 Schliep, Alexander, 1 19 Schmidt-Thieme, Lars, 171, 525, 533 Scholz, Sören W., 447 Schröder, Jan, 35 5 Schulz, Sascha, 39 7 Schwaiger, Manfred,...
Data Analysis Machine Learning and Applications Episode 1 Part 1 doc
... 10 9 10 8 13 4 12 6 13 0 14 0 13 8 17 9 16 6 14 41 0.7 15 3 200 19 8 16 8 17 7 16 0 17 3 17 2 18 5 18 7 17 39 22 18 10 7 12 7 12 9 16 2 54 55 71 1 31 116 15 0 10 3 10 4 14 7 14 6 14 9 11 9 10 5 61 62 14 4 12 2 10 1 11 7 13 3 11 1 ... 15 4 18 0 200 17 5 17 8 17 6 19 9 15 0 12 4 77 0.6 15 3 17 4 18 6 15 1 14 8 12 0 14 3 10 8 11 4 13 5...
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 SMOLA, ... Networks, 12 (5), 987–997 TITSIAS, M K and LIKAS, A (20 02) : Mixtures of Experts Classification Using a Hierarchical Mixture Model Neural Computation, 14 , 22 21 22 44...
Data Analysis Machine Learning and Applications Episode 1 Part 3 docx
... Classification Time 03 : 13 .60 00 :14 . 73 00 :14 . 63 Classif Accuracy % 95.78 % 91. 01 % 91. 01 % USPS (Min-Max) RBF Kernel 17 5 51. 5 5 51. 5 1. 37 13 .14 1. 05 1. 04 13 . 23 1. 05 H1-SVM H1-SVM RBF/H1 RBF/H1 Gr-Heu Gr-Heu ... taken 200 200 18 0 18 0 16 0 16 0 14 0 14 0 12 0 12 0 10 0 10 0 80 80 60 60 40 40 20 20 20 40 60 80 10 0 12 0 14 0 16 0 18 0 200 20 40 6...
Data Analysis Machine Learning and Applications Episode 1 Part 4 pptx
... 0.978 81 0.39823 1. 00000 0. 616 15 0.99 843 0.62620 1. 00000 0. 542 75 1. 00000 0.9 94 41 0.53337 96 .44 % 0.03 313 0 . 14 44 0 median 0.0 210 7 0.85 840 0.00088 0.99062 0.0 017 7 1. 00000 0. 342 31 0.90252 0 .18 1 94 0. 844 63 ... 0.53576 0.90705 0. 340 71 0.60606 0 .44 997 0.872 24 0.6 013 9 0. 848 88 0.608 21 0.8 718 3 0 .11 946 0.87399 0 .4 318 0 0.96372 0 .45 915 0.9 849 8...
Data Analysis Machine Learning and Applications Episode 1 Part 5 pdf
... COPK-Means ssALife with U*C 71 65. 7 10 0 96.4 55 .2 10 0 10 0 93.4 90 10 0 10 0 10 0 10 0 10 0 10 0 10 0 10 0 96.3 Fig Density defined clustering problem EngyTime: (a) partially labeled data (b) ssALife produced ... 0.626 0. 314 0 .56 6 0. 050 0 .11 2 0.062 0.680 0.600 15 1 = classes for the 0. 912 0. 452 0.366 0. 658 0.088 0.944 0.872 0.930 0. 310 0.390 0.006 0.060 0.008 0.02...
Data Analysis Machine Learning and Applications Episode 1 Part 6 docx
... repeat (c1 , c2 ) ← 10 : 11 : 12 : 13 : argmax c1 ,c2 ∈P∧(c1 ×c2 )∩Rcl = sim(c1 , c2 ) if sim(c1 , c2 ) ≥ then P ← (P \ {c1 , c2 }) ∪ {c1 ∪ c2 } end if until sim(c1 , c2 ) < 14 : return P 15 : end procedure ... Name) (x1 , x2 ) (x1 , x3 ) (x2 , x3 ) (Product Name) 0 .6 0 .1 0.0 0 0.0 76 0.849 0. 860 Feature Vector P[xi ≡ x j ] (0 .6, 1, 0.0 76) (0 .1, 0, 0.849) (0.0, 0, 0. 860 ) 0.8...
Data Analysis Machine Learning and Applications Episode 1 Part 7 doc
... follows: (t +1) (t +1) K |··· ∼ W 1( t +1) |··· k ∼ N (nk ⎛ , k =1 | · · · ∼ D( + n1 , , + nK ), (t +1) |··· k (t) 1 1 ) k + 2g , (2h + (t) 1 k + ) 1 (nk ∼ W ⎝2 + nk , (2 (t +1) (t) 1 yk + k + i:zi ... 363– 375 11 8 Marco Di Zio and Ugo Guarnera DI ZIO, M., GUARNERA, U and LUZI, O (20 07) : Imputation through finite Gaussian mixture models Computational Statistics and Data Anal...
Data Analysis Machine Learning and Applications Episode 1 Part 8 ppsx
... Kerf1 =1 Tie =1 Walle=3 Pin=3 Black=2 Walle=2 Kerf2 =1 Pin =1 Hat =1 Keyri =1 Sculp =1 Umbre =1 Trays =1 Fem-T =1 Light =1 MetWa =1 Cap =1 Trayp=3 Silve=3 Hat=3 Trays=3 Umbre=3 Factor - 14 .13 % Tie=3 SkinW =1 ... Application 18 9 umbh Umbrella Hat Tie Kerchief1 Kerchief2 T shirt T shirt V Sweater Cap Trayplas Trayleather Backpack Bag Cup 0.75 0.90 0.72 0 .88 0. 91 0 .85 0 .8...
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 , MacGregor, 19 97 ) In particular, we focus on the approach based on PLS components proposed by Kourti and MacGregor ... In particular, the bootstrap approach to estimate control 202 Rosaria Lombardo, Amalia Vanacore and Jean-Francçois Durand limits (Wu and Wang, 19 97 ; Jones a...
Data Analysis Machine Learning and Applications Episode 1 Part 10 ppt
... viol c4–c4 c4–e4 c4–g4 c4–a4 c4–c5 1 1 1 1 1 1 1∗ 1 1∗ 1 1 instrument notes flu guit pian trum viol c4–c4 c4–e4 c4–g4 c4–a4 c4–c5 1 1 1 1 1 1 1∗ 1 1 1∗ 1 1 4.3 Results with extended polyphonic ... 68,50 75 ,10 tion Optimality 97,40 96,60 96 ,10 96 ,10 96 ,10 81, 90 90,40 91, 00 90,50 Note: TCCS, POLCOURT and SCSCIENCE stand for “Teacher, Clerck and Civil Servan...
Data Analysis Machine Learning and Applications Episode 2 Part 1 pot
... watermark database Table Averaged precision and recall at N /2 for the watermark database Classes 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 92 243 21 4 14 4 10 9 24 4 17 3 ... 600 -400 log likelihood -20 0 27 5 -800 -10 00 - 12 0 0 10 0 passing aborted passing follow -14 00 -16 00 20 0 10 -10 0 15 20 25 30 time (s) -20 0 passing...
Data Analysis Machine Learning and Applications Episode 2 Part 2 ppsx
... al 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) ... Intelligent Data Analysis, 6(3) :23 7 25 5 KAM, P.-S and FU, A W.-C (20 00): Discovering Temporal Patterns for Interval-Based Events In: Data Warehousing and...
Data Analysis Machine Learning and Applications Episode 2 Part 3 pps
... WESTERHEIDE 20 0001 BESTEWEIDE 20 00 02 WESTERWELLE asoundex.lname W 236 B 233 W 236 3. 3 Candidate selection candidates (data1 , data2 , method, selvars1, selvars2, key1, key2, ) provides an interface ... [%] W2W2, =0.5 0.7 0.6 0.5 0.4 0 .3 0 .2 0.7 W1W1, =0.5 0.6 W W , =0.5 0.5 W2W2, =0.5 0.4 W W , =0.5 2 0 .3 0 .2 0.1 W2W2, =0.5 0 .2 W1W1, =0.5 0.5 0.4 W W , =0.5 power 30 7 0....
Data Analysis Machine Learning and Applications Episode 2 Part 4 doc
... 38 49 37 39 41 35 25 20 37 Complexity J4.8 J4.8 17 J4.8(cv) 18 RPart0 18 RPart1 15 QUEST 18 CTree 86 J4.8(cv) RPart0 RPart1 QUEST CTree 0 0 0 18 0 13 15 18 16 14 15 13 10 14 64 24 42 43 25 64 ... percent) J4.8(cv) J4.8 RPart0 J4.8 RPart1 J4.8 QUEST J4.8 CTree J4.8 RPart0 J4.8(cv) RPart1 J4.8(cv) QUEST J4.8(cv) CTree J4.8(cv) RPart1 RPart0 QUEST RPart0 CTree RPart0 QUEST RPart1 CTr...