VOCABULARY 1: LEARNING AND DOING

VOCABULARY 1: LEARNING AND DOING

VOCABULARY 1: LEARNING AND DOING
... I’ll ask Jeff to pick up an extra copy of the handout and I can borrow his lecture notes_ Professor Barnes is the only lecturer who gives handouts and his reading lists really save me a lot of ... (12.) doing a postgraduate degree, but decided it was time to get a job and earn some money (Most people go to state schools but some parents pay to send their children to private schools In England ... (1.)uniform There were lots of (2.) rules and the teachers were very (3.) strict We had to stand up whenever a teacher came into the room Once a week we had a (4.) test and anybody who got a (5.) grade...
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An investigation into learning and teaching english vocabulary at cua lo hihg school and some suggested activities to help students learn better

An investigation into learning and teaching english vocabulary at cua lo hihg school and some suggested activities to help students learn better
... ones that encourage me to choose the thesis: An investigation into learning and teaching English vocabulary at Cua Lo high school and some suggested activities to help students learn better Aims ... === an investigation into learning and teaching english vocabulary at cua lo high school and some suggested activities to help students learn better (khảo sát việc dạy học từ vựng tiếng anh trờng ... situations of learning and teaching English vocabulary at Cua Lo high school in particular and at Vietnamese high schools in general? How can the quality of teaching and learning English vocabulary...
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An investigation into some approaches to vocabulary teaching and learning and the application of games in teaching and learning vocabulary at pre – intermediate level at foreign language center – haiphong university

An investigation into some approaches to vocabulary teaching and learning and the application of games in teaching and learning vocabulary at pre – intermediate level at foreign language center – haiphong university
... situation of teaching and learning vocabulary at PreIntermediate level at FLC-HPU like? - What are the main difficulties in vocabulary teaching and learning at PreIntermediate Level at FLC-HPU? - What ... for teaching and learning vocabulary, some advantages of the use of games in vocabulary teaching and learning are also mentioned Chapter two deals with the investigation into teaching and learning ... relevant to the study i.e definition of vocabulary, concept of words, words and meaning, the status of vocabulary in language teaching and learning in the past and in recent years Some strategies...
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Slide an investigation into some approaches to vocabulary teaching and learning and the application of games in teaching and learning vocabulary at pre – intermediate level at foreign language center – haiphong university

Slide an investigation into some approaches to vocabulary teaching and learning and the application of games in teaching and learning vocabulary at pre – intermediate level at foreign language center – haiphong university
... Intermediate level at FLC-HPU?  What is the main reasons of the difficulties in vocabulary teaching and learning at Pre Intermediate level at FLC-HPU?  Do games help learners at Pre Intermediate ... language teaching and their relevance to vocabulary 1.4 Recent research about teaching and learning second language vocabulary 1.5 Advantages of the use games in vocabulary teaching and learning Games ... teaching and their relevance to vocabulary 1.4 Recent research about teaching and learning second language vocabulary 1.5 Advantages of the use of games in vocabulary teaching and learning Part...
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Data Analysis Machine Learning and Applications Episode 1 Part 1 doc

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 10 1 14 4 11 1 14 5 11 6 0.7 18 9 19 4 15 9 10 6 14 9 10 9 14 0 95 36 12 3 12 1 0.8 18 1 18 8 16 7 16 3 16 6 10 4 14 7 86 0.9 18 5 11 8 ... 10 8 13 4 12 6 13 0 14 0 13 8 0.368 17 5 13 2 19 3 13 9 15 8 15 3 200 19 7 19 4 19 6 19 9 19 8 16 8 17 7 16 0 17 0 15 1 17 3 17 2 18 5 18 1 18 7 16 1 16 9 17 9 16 6 16 5 16 3 15 6 19 1 18 4 58 15 7 18 8 18 2 18 9 70 17 6 15 2 15 4 17 4 18 0...
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Data Analysis Machine Learning and Applications Episode 1 Part 2 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 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 TUTZ, G and BINDER H (20 05): Localized ... maximum densities 0.000 0. 026 0 .19 3 0.000 0 .14 8 0. 713 f j = j, j 0.000 0.4 41 0.8 21 GCC = M f j = j, j 0.000 0.054 0. 21 7 GCC = M · K 0.000 0.0 31 0 .20 7 = 5, n = 500 0.000 0.0 42 0 .20 5 Sk = M f k j = k...
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Data Analysis Machine Learning and Applications Episode 1 Part 3 docx

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 60 80 10 0 12 0 14 0 16 0 18 0 200 Fig Problem fourclass (Schoelkopf and Smola 2002) ... 2.26% of the cases 0.00 1. 92 26. 83 71. 25 0.00 8. 31 79.80 11 .89 4.20 70 .18 22.26 3. 35 48 .33 49 .17 0.00 2.50 0.00 2.62 28. 83 68.54 0.28 3. 87 88. 41 7.44 2 .10 77 .30 18 .06 2.54 50. 83 46.67 2.50 0.00 LVQ...
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Data Analysis Machine Learning and Applications Episode 1 Part 4 pptx

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 0. 41 1 52 1. 00000 0.88097 0 .4 41 1 9 98.00% 0. 043 94 0.08223 single 0.00022 ... 0.3 543 5 0. 5 14 87 0 .47 083 0.6 210 2 0 .49 842 0.79 644 0.5 642 6 0. 813 95 0 .11 883 0.77503 0.367 71 0.95507 0. 311 34 0.980 24 0. 346 63 1. 00000 0.763 84 0.39900 89.56% 0,0 310 6 0 ,12 355 median 0.00527 0.3 04 51 0. 046 25...
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Data Analysis Machine Learning and Applications Episode 1 Part 5 pdf

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 0.028 0.346 0.006 0. 016 0.008 0. 010 0. 010 Applications ... Applications 3 .1 Gordon-Vichi macroeconomic ensemble Gordon and Vichi (20 01, Table 1) provide soft partitions of 21 countries based on macroeconomic data for the years 19 75, 19 80, 19 85, 19 90, and 19 95 These...
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Data Analysis Machine Learning and Applications Episode 1 Part 6 docx

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 0.2 0 .1 (Price) 4 .1 Collective ... 9 01 912 GAUL, W and SCHADER, M (19 88): Clusterwise aggregation of relations Applied Stochastic Models and Data Analysis, 4, 273–282 15 4 Kurt Hornik and Walter Böhm GORDON, A D and VICHI, M (19 98):...
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Data Analysis Machine Learning and Applications Episode 1 Part 7 doc

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 Analysis, 51, 5305–5 316 ... of Y1 , the nonresponse probabilities for (Y2 ,Y3 ) are 0 .1 if y1 < q1 , 0.2 if y1 ∈ [q1 , q2 ), 0.4 if y1 ∈ [q2 , q3 ) and 0.5 if y1 ≥ q3 The sample u is multiply imputed (m=5) via GMM Data...
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Data Analysis Machine Learning and Applications Episode 1 Part 8 ppsx

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 .83 0 .85 0. 78 0. 68 0.79 0. 78 0.77 0.72 0 .88 ... among the 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) Data Mining of an On-line Survey...
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Data Analysis Machine Learning and Applications Episode 1 Part 9 doc

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 and Woodall, 19 98 ; Liu and Tang, 19 96 ) ... données La Revue de Modulad, 31, 1 31 D’ AMBRA, L and LAURO, N ( 19 89) : Non symetrical analysis of three-way contingency tables Multiway Data Analysis, 3 01 315 ESCOFIER, B ( 19 83): Généralisation de...
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Data Analysis Machine Learning and Applications Episode 1 Part 10 ppt

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 Servant”, “Politics & Court” and “Scolarship ... 1 1 0 1 0 1 0 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 1 330, 390, 440 and 523 Hz) out of two groups of instruments, string instruments and wind...
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Data Analysis Machine Learning and Applications Episode 2 Part 1 pot

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 vs follow 10 12 14 16 18 20 22 time (s) Fig (Left) ... 24 3 21 4 14 4 10 9 24 4 17 3 097 4 42 068 19 0 8 02 556 28 3 R(N /2) 528 13 9 3 02 19 7 088 1 82 1 52 19 1 26 3 0 61 143 710 3 52 3 61 Classification and Retrieval of Ancient Watermarks 24 3 Fig Retrieval result of...
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