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www.buchsys.ahs.tu-dortmund.de/angebote/aktueller_zeitraum/_Finaltag_20__Juli_2026.html

Sportangebot - HSP - TU Dortmund

Buchung Booking 1-226-5000 Finaltag 20. Juli 2026 Beachvolleyball-Turnier (2er Teams) 1-226-5000 Finaltag 20. Juli 2026 Beachvolleyball-Turnier (2er Teams) 20.07.2026 Mo 16:00-22:00 BEP1 OH3 Mo 16:00-22:00 [...] Beschäftigte 3 € für Externe 1-226-5001 Finaltag 20. Juli 2026 Padel-Turnier (2er Teams) 1-226-5001 Finaltag 20. Juli 2026 Padel-Turnier (2er Teams) 20.07.2026 Mo 16:00-22:00 PP1-OH3 Mo 16:00-22:00 PP2-OH3 [...] 07.2026 Mo 17:35-18:20 TAP1 OH3 Mo 17:35-18:20 TAP2 OH3 20.07. Clarissa Goldmund entgeltfrei entgeltfrei / free of charge 1-226-5005 Finaltag 20. Juli 2026 Fußballturnier fA/F 1-226-5005 Finaltag 20. Juli …
ls3-www.cs.tu-dortmund.de/cms/medien/publikationen/PG361-Endbericht.pdf

Endbericht der Projektgruppe 361 Dragon Slayer SWORD

GRUNDLEGENDE KLASSEN [3] T1 MUX_2 T2 MUX_1 T3 COND_1 T4 SEM_1 MUX_3 Abbildung 4.1: Beispielausgabe des RessourcAllocationGraphs 4.2.7 Beispiel Siehe Abbildung 4.1 • Thread 1 ḧalt den Mutex 1 und wartet auf den [...] h dokumentiert werden. II Inhaltsverzeichnis 1 Einleitung 1 1.1 Aufgaben und Ziele der Projektgruppe . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Einleitung und Struktur . . . . . . . . . . [...] Klassen 11 4.1 Basisklassen der Kommunikation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.1.1 IOBaseWrapper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.1.2 IODS3Socket …
dm.cs.tu-dortmund.de/mlbits/class-ensembles/

Ensembles and Random Forests – Lecture Notes

can also use multiple classifiers \(F_i(x)\) What is the error rate of \[F(x) = \tfrac {1}{n}\sum \nolimits _{i=1}^n F_i(x) ? \quad ?\] if all \(F_i\) are the same, we do not gain anything if all \(F_i\) [...] the training data train a classifier \(K_i\) on each subsample combine decisions: \(K(x) = \frac1k \sum _{i=1}^k K_i(x)\) Compare to .632 bootstrap evaluation! Random Forests [ Brei01 ; Ho95 ] Idea: use more [...] [Diet00] Dietterich, T.G. 2000. Ensemble methods in machine learning . Proc. 1st int. Workshop on multiple classifier systems MCS (2000), 1–15. [Ho95] Ho, T.K. 1995. Random decision forests . ICDAR (1995), 278–282 …
dm.cs.tu-dortmund.de/mlbits/matrix-factorization-kmeans/

k-Means as Matrix Factorization – Lecture Notes

uster assignment}} \end{align*}\] where \(H\) is an indicator matrix ( \(H\in \{ 0,1\} ^{k\times n}\) , exactly one 1 per column). A very strong constraint on the shape of \(H\) ! In the image example: [...] \(k\) -means are the arithmetic mean of the assigned \(x_i\) , we have \(W=XH^T(HH^T)^{-1}\) , use \(Y:=H^T(HH^T)^{-1}\) and we can rewrite \[\begin{align*} \textcolor[RGB]{132,184,24}{X} \approx WH & = [...] k}}\qquad \geq 0 & \qquad & \text{s.t. } \forall _j \sum \nolimits _i y_{ij} = 1 \; & \wedge & \; \forall _{i,j} y_{ij}\kern 1pt\in \{ 0,\text{const}\} \\ \textcolor[RGB]{0,155,170}{H} & \in \mathbb {R}\mathrlap …
dm.cs.tu-dortmund.de/mlbits/frequent-pattern-fptree-mining/

Mining the Frequent Pattern Tree – Lecture Notes

and the count in the CPB of \(i\) item CPB \(p\) \(fcam:2\) , \(cbm:1\) \(m\) \(fca:3\) , \(cb:1\) \(b\) \(fca:1\) , \(f:2\) , \(c:1\) \(a\) \(fc:4\) \(c\) \(f:4\) \(f\) \(\{ \}\) Construction of the [...] trees using an additional item ( \(i_2i_1\) conditional tree, \(i_3i_2i_1\) conditional …) ⇝ smaller trees, that only contain transactions that contain \(i_1\) and \(i_2\) and \(i_3\) … Given a conditional [...] conditional FP-tree \(\mathllap {\Rightarrow \quad }\) \(m\) -conditional pattern base: \(fca:3\) , \(cb:1\) \(\Downarrow\) \(m\) -conditional FP-tree: (header table omitted) Recursive Mining of FP-trees If …
dm.cs.tu-dortmund.de/en/mlbits/class-ensembles/

Ensembles and Random Forests – Lecture Notes

can also use multiple classifiers \(F_i(x)\) What is the error rate of \[F(x) = \tfrac {1}{n}\sum \nolimits _{i=1}^n F_i(x) ? \quad ?\] if all \(F_i\) are the same, we do not gain anything if all \(F_i\) [...] the training data train a classifier \(K_i\) on each subsample combine decisions: \(K(x) = \frac1k \sum _{i=1}^k K_i(x)\) Compare to .632 bootstrap evaluation! Random Forests [ Brei01 ; Ho95 ] Idea: use more [...] [Diet00] Dietterich, T.G. 2000. Ensemble methods in machine learning . Proc. 1st int. Workshop on multiple classifier systems MCS (2000), 1–15. [Ho95] Ho, T.K. 1995. Random decision forests . ICDAR (1995), 278–282 …
dm.cs.tu-dortmund.de/en/mlbits/frequent-pattern-fptree-mining/

Mining the Frequent Pattern Tree – Lecture Notes

and the count in the CPB of \(i\) item CPB \(p\) \(fcam:2\) , \(cbm:1\) \(m\) \(fca:3\) , \(cb:1\) \(b\) \(fca:1\) , \(f:2\) , \(c:1\) \(a\) \(fc:4\) \(c\) \(f:4\) \(f\) \(\{ \}\) Construction of the [...] trees using an additional item ( \(i_2i_1\) conditional tree, \(i_3i_2i_1\) conditional …) ⇝ smaller trees, that only contain transactions that contain \(i_1\) and \(i_2\) and \(i_3\) … Given a conditional [...] conditional FP-tree \(\mathllap {\Rightarrow \quad }\) \(m\) -conditional pattern base: \(fca:3\) , \(cb:1\) \(\Downarrow\) \(m\) -conditional FP-tree: (header table omitted) Recursive Mining of FP-trees If …
dm.cs.tu-dortmund.de/en/mlbits/matrix-factorization-kmeans/

k-Means as Matrix Factorization – Lecture Notes

uster assignment}} \end{align*}\] where \(H\) is an indicator matrix ( \(H\in \{ 0,1\} ^{k\times n}\) , exactly one 1 per column). A very strong constraint on the shape of \(H\) ! In the image example: [...] \(k\) -means are the arithmetic mean of the assigned \(x_i\) , we have \(W=XH^T(HH^T)^{-1}\) , use \(Y:=H^T(HH^T)^{-1}\) and we can rewrite \[\begin{align*} \textcolor[RGB]{132,184,24}{X} \approx WH & = [...] k}}\qquad \geq 0 & \qquad & \text{s.t. } \forall _j \sum \nolimits _i y_{ij} = 1 \; & \wedge & \; \forall _{i,j} y_{ij}\kern 1pt\in \{ 0,\text{const}\} \\ \textcolor[RGB]{0,155,170}{H} & \in \mathbb {R}\mathrlap …
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Prof. Dr. Miriam Seyda

für Sportpädagogische Forschung, 1 , 5-20. Seyda, M. (2016). Potenziale einer Selbstkonzeptförderung von Heranwachsenden in und durch Schulsport. Bewegung & Sport, 70 (1), 28-31. Seyda, M. & Meier, H.E [...] schools. Journal of Physical Education and Health, 6 , 5-14. Seyda, M. (2015). Editorial. motorik, 38 (1). 1-2. Seyda. M. (2013). Ziele einer Selbstkonzeptförderung von Kindern durch Bewegung. motorik, 36 (2) [...] Sportdidaktik Prof. Dr. Miriam Seyda Leitung des Arbeitsbereichs 3: Sportdidaktik Raum 1.119 Otto-Hahn-Straße 3 44227 Dortmund Tel: +49 231 755-4108 Fax: +49 231 755-4105 E-Mail Sprechzeiten im SoSe 2026 …
www-ai.cs.tu-dortmund.de/de/LEHRE/PG/PG343/javadoc/dataanalysis/classifiers/NetEntityTypeClassifier.html

: Class NetEntityTypeClassifier

toString in class Classifier Since: 1.1 getpositiveClassNames public java.lang.String getpositiveClassNames () Liefert eine Kopie der positiven Schluesselworte zurueck. Since: 1.1 Overview Package Class [...] so werden alle davon erbenden Klassen sowie die Klasse selbst als positiv klassifiziert. Version: 1.1 (12.11.1999) Author: Andre Masloch See Also: Serialized Form Field Summary protected java.lang.String [...] Elternklassen dieser NetEntity einem Namen entsprechen, mit dem der Klassifizierer trainiert wurde, so wird 1.0 eingetragen, ansonsten 0.0. Wird der Klassifizierer z.B. mit "dataprovider.net.TextNetResult" trainiert …

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Endbericht der Projektgruppe 361 Dragon Slayer SWORD

Ensembles and Random Forests – Lecture Notes

k-Means as Matrix Factorization – Lecture Notes

Mining the Frequent Pattern Tree – Lecture Notes

Ensembles and Random Forests – Lecture Notes

Mining the Frequent Pattern Tree – Lecture Notes

k-Means as Matrix Factorization – Lecture Notes

Prof. Dr. Miriam Seyda

: Class NetEntityTypeClassifier

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Sportangebot - HSP - TU Dortmund

Endbericht der Projektgruppe 361 Dragon Slayer SWORD

Ensembles and Random Forests – Lecture Notes

k-Means as Matrix Factorization – Lecture Notes

Mining the Frequent Pattern Tree – Lecture Notes

Ensembles and Random Forests – Lecture Notes

Mining the Frequent Pattern Tree – Lecture Notes

k-Means as Matrix Factorization – Lecture Notes

Prof. Dr. Miriam Seyda

: Class NetEntityTypeClassifier