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ORCIDNikou Günnemann
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2010 – 2019
- 2018
[b1]Nikou Günnemann:
Machine Learning Methods for Detecting Rare Events in Temporal Data (Maschinelles Lernen zur Entdeckung seltener Ereignisse in temporalen Daten). Technical University of Munich, Germany, 2018- 2017
- [c8]Nikou Günnemann, Jürgen Pfeffer:
Cost Matters: A New Example-Dependent Cost-Sensitive Logistic Regression Model. PAKDD (1) 2017: 210-222 - [c7]Nikou Günnemann, Jürgen Pfeffer:
Predicting Defective Engines using Convolutional Neural Networks on Temporal Vibration Signals. LIDTA@PKDD/ECML 2017: 92-102 - 2015
- [c6]Nikou Günnemann, Jürgen Pfeffer:
Finding Non-Redundant Multi-Word Events on Twitter. ASONAM 2015: 520-525 - 2014
- [c5]Michael Derntl
, Nikou Günnemann, Alexander Tillmann, Ralf Klamma, Matthias Jarke:
Building and Exploring Dynamic Topic Models on the Web. CIKM 2014: 2012-2014 - [c4]Stephan Günnemann, Nikou Günnemann, Christos Faloutsos:
Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution. KDD 2014: 841-850 - [c3]Nikou Günnemann, Stephan Günnemann, Christos Faloutsos:
Robust multivariate autoregression for anomaly detection in dynamic product ratings. WWW 2014: 361-372 - 2013
- [j1]Nikou Günnemann, Michael Derntl, Ralf Klamma, Matthias Jarke:
An Interactive System for Visual Analytics of Dynamic Topic Models. Datenbank-Spektrum 13(3): 213-223 (2013) - [c2]Nikou Günnemann:
D-VITA: A Visual Interactive Text Analysis System Using Dynamic Topic Mining. BTW Workshops 2013: 237-246 - [c1]Michael Derntl, Nikou Günnemann, Ralf Klamma:
A Dynamic Topic Model of Learning Analytics Research. LAK (Data Challenge) 2013
Coauthor Index
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