Microsoft Word - ModellevonEuler2023Artikel2

0.0913 1 0.1229 1 0.1043 1 2-4 0.0839 1.0884 0.1085 1.1331 0.0952 1.0961 4-6 0.0771 1.1846 0.0957 1.2840 0.0868 1.2015 6-8 0.0708 1.2893 0.0845 1.4550 0.0792 1.3170 8-10 0.0651 1.4032 0.0745 1.6487 0.0723 [...] 12 13 14 150; 1; 1; 1; 0m m m m m     0 3R  1.0883  II 25 11 12 13 14 151; 1; 1; 1; 1m m m m m     0 5R  1.1332  III 20 11 12 13 14 151; 1; 1; 0; 0m m m m m     0 3R  1.0961  In Model [...] complex numbers i=1,2,3 Model I Model II Model III i Re(i) Im(i) Modulus ri Re(i) Im(i) Modulus ri Re(i) Im(i) Modulus ri 1 1.0884 0 1.0884 1.1331 1.1331 1.0961 0 1.0961 2 0.9568 0.5046 1.0817 0.9791 0 …

GSTT1 and gastric cancer, a meta-analysis

8.51 1.09 0.90-1.32 12.49 1.05 0.79-1.39 10.96 1.09 0.91-1.30 Adulthood 7 7.41 1.04 0.83-1.29 11.83 1.09 0.81-1.49 10.47 1.06 0.87-1.30 Childhood 1 0.25 1.28 0.84-1.96 0.001 0.80 0.37-1.74 0.21 1.19 0 [...] CI All of studies 19.648* 10 1.09 0.96-1.23 Adulthood 13.806 6 1.05 0.91-1.21 Childhood 5.056 2 1.21 0.93-1.58 Non-smokers 10.97* 4 1.12 0.94-1.32 Smokers 7.72 4 1.03 0.85-1.24 * There is heterogeneity [...] 0.79-1.78 Non-smokers 3 2.75 1.19 0.91-1.58 3.40 0.97 0.66-1.44 3.96 1.16 0.89-1.51 Smokers 4 3.71 0.94 0.69-1.27 8.36 1.10 0.72-1.70 5.37 0.98 0.75-1.29 Note: There is no heterogeneity between studies …

Technische Universität Dortmund Closed Sets Web Mining Closed Item Sets A B C D 1 1 1 1 0 1 1 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 1 0 closure(S) ist die maximale Obermenge (gemäß der Teilmengenbeziehung) von S [...] Universität Dortmund Closed Sets Web Mining Beispiel mit Frequency threshold 0.3 A B C D 1 0 1 0 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1 0 {} {B}{A} {C} {D} {A,B} {A,C} {A,D} {B,C} {B,D} {C,D} {A,B,C} {A,B,D} {A,C,D} {B [...] k + 1-großen häufigen Subgraphen Sk+1 Sk+1 := {} for gi ∈ Sk do for gj ∈ Skdo for g = gi gjdo if support(g) ≥ minsup, g /∈ Sk+1 then Sk+1 := Sk+1 ∪ g if Sk+1 6= {} then call Apriori (D,minsup, Sk+1) return …

Vorlesung Wissensentdeckung - Closed Sets, Web Mining

Technische Universität Dortmund Closed Sets Web Mining Closed Item Sets A B C D 1 1 1 1 0 1 1 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 1 0 closure(S) ist die maximale Obermenge (gemäß der Teilmengenbeziehung) von S [...] Universität Dortmund Closed Sets Web Mining Beispiel mit Frequency threshold 0.3 A B C D 1 0 1 0 1 1 1 0 0 1 1 1 0 1 0 1 1 1 1 0 {} {B}{A} {C} {D} {A,B} {A,C} {A,D} {B,C} {B,D} {C,D} {A,B,C} {A,B,D} {A,C,D} {B [...] k + 1-großen häufigen Subgraphen Sk+1 Sk+1 := {} for gi ∈ Sk do for gj ∈ Skdo for g = gi gjdo if support(g) ≥ minsup, g /∈ Sk+1 then Sk+1 := Sk+1 ∪ g if Sk+1 6= {} then call Apriori (D,minsup, Sk+1) return …

Vorpommern 43.1 44.1 44.9 49.5 50.7 51.7 53.6 54.5 56.1 56.0 56.0 56.0 56.4 56.9 57.6 57.9 58.6 59.2 60.3 Niedersachsen 5.1 6.9 9.1 11.9 15.8 18.6 22.1 24.4 27.9 28.3 28.4 29.6 30.9 32.1 32.9 31.9 33.8 [...] 93.2 94.1 95.2 95.1 95.8 95.9 96.0 95.2 96.3 95.4 95.2 94.9 94.9 95.7 95.4 95.5 94.5 94.7 Niedersachsen 79.3 83.9 86.1 88.2 89.6 91.6 92.6 93.7 93.6 94.8 93.2 93.1 92.7 92.6 92.0 91.5 91.7 91.1 91.8 No [...] 5 52.1 52.7 52.3 53.3 54.2 55.2 Baden-Württemberg 8.7 11.5 13.6 15.8 18.3 20.8 23.1 24.9 27.8 27.8 27.7 28.6 29.1 29.5 30.0 28.7 29.9 31.0 32.0 Bayern 8.2 10.7 13.2 15.7 18.5 20.6 23.0 24.8 27.1 27.5 27 …

Maximum Margin Hyperplane – Lecture Notes

between \(B_1\) and \(B_2\) : Support Vectors \(\vec{x}_1\) , \(\vec{x}_2\) \(\vec{x}_1\) in class \(+1\) on \(B_{+1}\) \(\vec{x}_2\) in class \(-1\) on \(B_{-1}\) \(\vec{w} \cdot (\vec{x}_1 - \vec{x}_2) [...] Positive margin: \(B_{+1}: \quad \left\langle \vec{w},\vec{x}\mathstrut \right\rangle + b = +1\) Negative margin: \(B_{-1}: \quad \left\langle \vec{w},\vec{x}\mathstrut \right\rangle + b = -1\) Boundary of the [...] mathstrut \right\rangle + b & \geq +1 \qquad \text{if } y_i = +1, \\ \left\langle \vec{w},\vec{x_i}\mathstrut \right\rangle + b & \leq -1 \qquad \text{if } y_i = -1, \end{align*}\] implicit here: no m …

Maximum Margin Hyperplane – Lecture Notes

between \(B_1\) and \(B_2\) : Support Vectors \(\vec{x}_1\) , \(\vec{x}_2\) \(\vec{x}_1\) in class \(+1\) on \(B_{+1}\) \(\vec{x}_2\) in class \(-1\) on \(B_{-1}\) \(\vec{w} \cdot (\vec{x}_1 - \vec{x}_2) [...] Positive margin: \(B_{+1}: \quad \left\langle \vec{w},\vec{x}\mathstrut \right\rangle + b = +1\) Negative margin: \(B_{-1}: \quad \left\langle \vec{w},\vec{x}\mathstrut \right\rangle + b = -1\) Boundary of the [...] mathstrut \right\rangle + b & \geq +1 \qquad \text{if } y_i = +1, \\ \left\langle \vec{w},\vec{x_i}\mathstrut \right\rangle + b & \leq -1 \qquad \text{if } y_i = -1, \end{align*}\] implicit here: no m …