Microsoft Word - peter_pflaumer_09112019_final

People alive 0 0 2 0 2 2 2 4 0 4 4 2 6 0 6 6 2 8 0 8 8 0 8 0 8 10 0 8 0 8 12 0 8 0 8 14 0 8 0 8 16 0 8 0 8 18 0 8 0 8 20 0 8 2 6 22 0 8 2 6 24 2 10 2 8 26 4 14 2 12 28 6 20 2 18 30 4 24 2 22 … 216 13530 [...] year 500, 235 and, in year 1000, 2.2753·1012 (2.3 trillion) or 2,275,313 people per sq. m. 5 0 20 40 60 80 100 0 2 0 0 4 0 0 Year P o p u la tio n 0 20 40 60 80 100 -0 .2 0 .2 0 .6 Year G ro w th R a te Fig [...] tendency of achieving stability. 10 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 100 200 300 400 500 600 700 800 900 1000 Fig. 5a: Ratio T t tP P lR -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 0 100 200 300 400 500 600 …

ohne Berlin Jugendarbeit 4.011 17 83 13 11 2 31 1 Ju’sozialarbeit 1.107 10 90 12 19 8 35 2 HzE, ambulant 2.756 5 95 36 15 4 23 2 HzE, stationär 6.767 3 97 25 17 2 30 6 Tätige Personen in der Kinder- und [...] Ausrichtung 3 5,0 1,4 1,7 63,2 22,9 5,9 Kinder- und Jugendarbeit Jugendsozialarbeit Förderung der Erziehung in der Familie (1) Kindertagesbetreuung zusammen Hilfen zur Erziehung (2) Sonstiges (3) Ausgaben insgesamt [...] Erziehung auf ihrem Weg der Pluralisierung 5 '27,2er-Hilfen' (stationär); 0,4 Heimerziehung; 10,0 Vollzeitpflege; 7,9 ISE; 0,6 Tagesgruppen- erziehung; 2,6 SPFH; 20,8 Betreuungshelfer ; 1,1 Erziehungs- …

Maximum Margin Hyperplane – Lecture Notes

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 \(B_{-1}\) \(\vec{w} \cdot (\vec{x}_1 - \vec{x}_2) = 2\) Width of [...] i=1,2,\dots ,N \end{align*}\] maximizing the margin is equivalent to minimizing the following objective function: \[f(\vec{w}) = \tfrac {1}{2}\left\lVert \smash {\vec{w}}\mathstrut \right\rVert ^2\] continue [...] vector \(\mu _C\) for each class \(C\) Use \(\vec{w}:=\mu _{C_1}-\mu _{C_2}\) Use \(\vec{a}:=\tfrac 12\mu _{C_1}+\tfrac 12\mu _{C_2}\) Use orthogonal hyperplane through \(\vec{a}\) okay, but not actually …

Maximum Margin Hyperplane – Lecture Notes

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 \(B_{-1}\) \(\vec{w} \cdot (\vec{x}_1 - \vec{x}_2) = 2\) Width of [...] i=1,2,\dots ,N \end{align*}\] maximizing the margin is equivalent to minimizing the following objective function: \[f(\vec{w}) = \tfrac {1}{2}\left\lVert \smash {\vec{w}}\mathstrut \right\rVert ^2\] continue [...] vector \(\mu _C\) for each class \(C\) Use \(\vec{w}:=\mu _{C_1}-\mu _{C_2}\) Use \(\vec{a}:=\tfrac 12\mu _{C_1}+\tfrac 12\mu _{C_2}\) Use orthogonal hyperplane through \(\vec{a}\) okay, but not actually …

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/JPEG /ColorACSImageDict << /QFactor 0.76 /HSamples [2 1 1 2] /VSamples [2 1 1 2] >> /ColorImageDict << /QFactor 0.76 /HSamples [2 1 1 2] /VSamples [2 1 1 2] >> /JPEG2000ColorACSImageDict << /TileWidth 256 [...] /JPEG /GrayACSImageDict << /QFactor 0.76 /HSamples [2 1 1 2] /VSamples [2 1 1 2] >> /GrayImageDict << /QFactor 0.76 /HSamples [2 1 1 2] /VSamples [2 1 1 2] >> /JPEG2000GrayACSImageDict << /TileWidth 256 [...] the Round_Robin procedure in 2.2.2). Due to page limitations we refer the reader to [2,3] for further details. 3. Periodic Reinforcement Learning in DEZENT As explained in 2.2 negotiations are organized …

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