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ORCIDIngo Mierswa
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2010 – 2019
- 2016
[c18]Ingo Mierswa:
The Wisdom of Crowds: Best Practices for Data Prep & Machine Learning Derived from Millions of Data Science Workflows. KDD 2016: 411
2000 – 2009
- 2009
- [b1]Ingo Mierswa:
Non-Convex and Multi-Objective Optimization in Data Mining - Non-Convex and Multi-Objective Optimization for Statistical Learning and Numerical Feature Engineering. Technical University of Dortmund, Germany, 2009, pp. 1-264 - [j4]Ingo Mierswa:
Rapid Miner. Künstliche Intell. 23(2): 62-63 (2009) - [p1]Ingo Mierswa:
Nichtkonvexe und Multikriterielle Optimierung für Data Mining. Ausgezeichnete Informatikdissertationen 2009: 151-160 - 2008
- [j3]Ingo Mierswa, Katharina Morik:
About the non-convex optimization problem induced by non-positive semidefinite kernel learning. Adv. Data Anal. Classif. 2(3): 241-258 (2008) - [c17]Christian Bockermann, Ingo Mierswa, Katharina Morik:
On the Automated Creation of Understandable Positive Security Models for Web Applications. LWA 2008: 12-17 - [c16]Christian Bockermann, Ingo Mierswa, Katharina Morik:
On the Automated Creation of Understandable Positive Security Models for Web Applications. PerCom 2008: 554-559 - 2007
- [c15]Ingo Mierswa:
Controlling overfitting with multi-objective support vector machines. GECCO 2007: 1830-1837 - [c14]Alexander Hinneburg, Ralf Klinkenberg, Ingo Mierswa, Stefan Posch, Steffen Neumann:
Workshop on Knowledge Discovery, Data Mining, and Machine Learning and Workshop on Bioinformatics. LWA 2007: 12 - [c13]Ingo Mierswa:
Regularization through Multi-Objective Optimization. LWA 2007: 94-101 - 2006
- [c12]Michael Wurst, Katharina Morik, Ingo Mierswa:
Localized Alternative Cluster Ensembles for Collaborative Structuring. ECML 2006: 485-496 - [c11]Ingo Mierswa, Michael Wurst:
Information preserving multi-objective feature selection for unsupervised learning. GECCO 2006: 1545-1552 - [c10]Ingo Mierswa:
Evolutionary learning with kernels: a generic solution for large margin problems. GECCO 2006: 1553-1560 - [c9]Fabian Mörchen, Ingo Mierswa, Alfred Ultsch:
Understandable models Of music collections based on exhaustive feature generation with temporal statistics. KDD 2006: 882-891 - [c8]Ingo Mierswa, Michael Wurst, Ralf Klinkenberg, Martin Scholz, Timm Euler:
YALE: rapid prototyping for complex data mining tasks. KDD 2006: 935-940 - [c7]Ingo Mierswa, Michael Wurst:
Sound Multi-objective Feature Space Transformation for Clustering. LWA 2006: 330-337 - 2005
- [j2]Ingo Mierswa, Katharina Morik:
Merkmalsextraktion aus Audiodaten Evolutionäre Aufzucht von Methodenbäumen. Inform. Spektrum 28(5): 381-388 (2005) - [j1]Ingo Mierswa, Katharina Morik:
Automatic Feature Extraction for Classifying Audio Data. Mach. Learn. 58(2-3): 127-149 (2005) - [c6]Ingo Mierswa, Michael Wurst:
Efficient Case Based Feature Construction. ECML 2005: 641-648 - [c5]Ingo Mierswa, Katharina Morik:
Method trees: building blocks for self-organizable representations of value series: how to evolve representations for classifying audio data. GECCO Workshops 2005: 293-300 - [c4]Ingo Mierswa:
Incorporating fuzzy knowledge into fitness: multiobjective evolutionary 3D design of process plants. GECCO 2005: 1985-1992 - [c3]Helge Homburg, Ingo Mierswa, Bülent Möller, Katharina Morik, Michael Wurst:
A Benchmark Dataset for Audio Classification and Clustering. ISMIR 2005: 528-531 - 2004
- [c2]Ingo Mierswa:
Automatic Feature Extraction from Large Time Series. GfKl 2004: 600-607 - [c1]Ingo Mierswa:
Automatic Feature Extraction from Large Time Series. LWA 2004: 187-192
Coauthor Index
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