instruction) 1. P4 Distribution 71,0 23,0 2. PR4, 1,1,2 Selection 27,5 16,3 3. C4,3 Distribution 26,7 26,9 4. AR4,3 Partition 6,0 3,0 5. P3 Selection 80,7 77,2 6. AR2,4 Distribution 7,4 13,0 7. PR2,2 Partition [...] ....... 289 8.4.3 Klassenerweiterung ...................................................................... 292 8.4.4 Kombination aus Klassenneubildung und Klassenerweiterung ... 296 8.4.5 Fazit ..... [...] Selektion C5,3 22,5 Partition C4,2 31,0 Permutation. o. Wh. Selektion P3 77,2 Distribution P4 23,9 Permutation. m. Wh. Selektion PR4,1,1,2 16,3 Distribution PR5, 1,1,3 10,6 Partition PR4,2,2 32,3 Variation. o. …

P:\TEX\SIOPT\19-4\70427\70427.dvi

0121 3.9 e−4 0.58 0.0127 1.9 e−4 0.69 0.0122 4.3 e−4 0.81 1,000 0.00228 3.7 e−5 5.8 0.00257 2.2 e−5 7.3 0.00271 4.5 e−5 8.5 2,000 0.00145 2.1 e−5 11.6 0.00166 1.0 e−5 13.8 0.00179 2.7 e−5 16.4 E-SA ε ( z̃N [...] 25 4,000 −7.9075 5 −5.9365 12 −7.2595 20 −5.5935 49 E-SA 100 −7.6895 0 −5.7988 1 −7.0165 1 −4.9364 4 1,000 −7.8559 2 −5.8919 4 −7.2029 7 −5.3895 20 2,000 −7.8737 3 −5.9067 7 −7.2306 15 −5.4870 39 4,000 [...] 2,000 yes 9.9978 0.2579 11.71 10.0486 0.2066 11.74 9.9830 0.1872 436.94 4.5. N-SA versus E-SA. The data in Tables 4.3, 4.4, and 4.6 demonstrate that with the same sample size N , the N-SA somehow outperforms …

inequality (4.21) is equivalent to∥∥∥∥∇uh −−∫ hB ∇uh ∥∥∥∥2 L2(Ωh;R3×3) ≤ C‖∇suh‖2L2(Ωh,R3×3 sym) . (4.22) 4.6. PROOF OF THE MOSCO-CONVERGENCE 51 hB h t+ h 2t+ h 3t+ h 0 t 2t 3t Figure 4.4: Illustration [...] . . . . . . . . . . 38 4.2 Summary of the setting . . . . . . . . . . . . . . . . . . . . . . 41 4.3 Description of the limit system . . . . . . . . . . . . . . . . . . 42 4.4 Statement of the convergence [...] Using the scaling matrix Sh := ( 1 1/h 1/h ) ∈ R3×3 sym , (4.2) 40 CHAPTER 4. ELASTOPLASTIC RODS we thus have u(x) = h2Shu h(Shx) . (4.3) With (4.3), we can now write the strain tensor as ∇su =  h2∂1u …

SVMstruct

F1 Const CPU(%QP) PCFG 61.4 90.4 55.2 86.0 N/A 0 SVMnonlinear 66.3 92.0 58.9 86.2 7494 1.2(81.6%) SVMstruct 1 62.2 92.1 58.9 88.5 8043 3.4(10.5%) SVMstruct 2 63.5 92.3 58.3 88.4 7117 3.5(18.0%) Regina Fritsch [...] Fehler Test Fehler Const Verlust SVMnonlinear 0.2 ± 0.1 5.1 ± 0.6 2824 ± 106 1.02 ± 0.01 SVMstruct 0.4 ± 0.4 5.1 ± 0.8 2626 ± 225 1.10 ± 0.08 die Ergebnisse sind vergleichbar Regina Fritsch SVMstruct 18 / 21 [...] Inhaltsverzeichnis 1 Einleitung 2 Diskriminanten und Verlustfunktionen 3 Margins und Margin-Maximierung 4 SVMstruct Algorithmus 5 Anwendungen und Experimente 6 Ergebnisse Regina Fritsch SVMstruct 2 / 21 Einleitung …

PowerPoint Presentation

50 35.02 53.72 4. 5 30 14.94 102.5 5 57.71 7.13 72.18 35.89 50.74 5. 10 10 1.66 105.0 3 32.75 6.44 79.43 37.96 55.39 6. 10 15 3.73 105.0 3 49.41 8.06 60.30 33.09 49.60 7. 10 20 6.64 105.0 4 69.46 8.79 46 [...] 1289,4x + 79,636 R2 = 0,86680 20 40 60 80 100 0,00 0,02 0,04 0,06 0,08 0,10 0,12 0,14 0,16 0,18 l n C Vave , % 10 kV 15 kV 20 kV 30 kV 5th International Conference on High Speed Forming – 2012 2 4 D Hlln [...] PowerPoint Presentation 4 R d h Schematic diagram of spherical dimple in a hole of membrane gauge where t – membrane thickness; R – radius of spherical segment; 1 – stress, at which deformation occurs; …

Exact Text Search and Ranked Retrieval – Lecture Notes

operator – usually 2–4× larger index) For TF-IDF, we can store the quantity/positions along with the document: bird → Doc 1 ×1, Doc 2 ×1 early → Doc 2 ×1, Doc 4 ×2 early → Doc 2 @ 4, Doc 4 @ 1,5 Ranked Retrieval [...] Model If we want to search for bird + early , this corresponds to using AND on these columns: Dim 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 a and bed bird but cheese early feather [...] that the to together wealthy wise worm Doc 1 1 1 1 1 1 1 Doc 2 1 1 1 1 1 1 1 1 Doc 3 1 1 1 1 1 1 Doc 4 1 1 1 1 1 1 1 1 1 1 1 intersection of bird AND early = 0011 AND 1010 = 0010 matching document: bit 2 …

Frequent Sequence Mining – Lecture Notes

F 2 15 A, B, F 2 20 E 3 10 A, B, F 4 10 D, G, H 4 20 B, F 4 25 A, G, H Frequent Sequences: Sequence Support A 4 B 4 D 2 F 4 A B 3 A F 3 B \(\rightarrow\) A 2 B F 4 D \(\rightarrow\) A 2 D \(\rightarrow\) …

Exact Text Search and Ranked Retrieval – Lecture Notes

operator – usually 2–4× larger index) For TF-IDF, we can store the quantity/positions along with the document: bird → Doc 1 ×1, Doc 2 ×1 early → Doc 2 ×1, Doc 4 ×2 early → Doc 2 @ 4, Doc 4 @ 1,5 Ranked Retrieval [...] Model If we want to search for bird + early , this corresponds to using AND on these columns: Dim 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 a and bed bird but cheese early feather [...] that the to together wealthy wise worm Doc 1 1 1 1 1 1 1 Doc 2 1 1 1 1 1 1 1 1 Doc 3 1 1 1 1 1 1 Doc 4 1 1 1 1 1 1 1 1 1 1 1 intersection of bird AND early = 0011 AND 1010 = 0010 matching document: bit 2 …