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2020 – today
- 2020
[c12]Cheng Feng
, Xiao Liang, Daniel Schneegass, Pengwei Tian:
RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning. CIKM 2020: 345-354- [i2]Cheng Feng, Xiao Liang, Daniel Schneegass, Pengwei Tian:
RelSen: An Optimization-based Framework for Simultaneous Sensor Reliability Monitoring and Process State Estimation. CoRR abs/2004.08762 (2020)
2010 – 2019
- 2019
- [i1]Changwei Loh, Daniel Schneegass, Pengwei Tian:
A Search for the Underlying Equation Governing Similar Systems. CoRR abs/1908.10673 (2019)
2000 – 2009
- 2009
- [j2]Daniel Schneegaß:
On the bias of batch Bellman residual minimisation. Neurocomputing 72(7-9): 2005-2008 (2009) - [j1]Thomas Martinetz, Kai Labusch, Daniel Schneegaß:
SoftDoubleMaxMinOver: Perceptron-Like Training of Support Vector Machines. IEEE Trans. Neural Networks 20(7): 1061-1072 (2009) - 2008
- [b1]Daniel Schneegaß:
Steigerung der Informationseffizienz im Reinforcement-Learning. University of Lübeck, 2008 - [c11]Alexander Hans, Daniel Schneegaß, Anton Maximilian Schäfer, Steffen Udluft:
Safe exploration for reinforcement learning. ESANN 2008: 143-148 - [c10]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Uncertainty propagation for quality assurance in Reinforcement Learning. IJCNN 2008: 2588-2595 - 2007
- [c9]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Neural Rewards Regression for near-optimal policy identification in Markovian and partial observable environments. ESANN 2007: 301-306 - [c8]Daniel Schneegaß, Anton Maximilian Schäfer, Thomas Martinetz:
The Intrinsic Recurrent Support Vector Machine. ESANN 2007: 325-330 - [c7]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Explicit Kernel Rewards Regression for data-efficient near-optimal policy identification. ESANN 2007: 337-342 - [c6]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Improving Optimality of Neural Rewards Regression for Data-Efficient Batch Near-Optimal Policy Identification. ICANN (1) 2007: 109-118 - [c5]Anton Maximilian Schäfer, Daniel Schneegaß, Volkmar Sterzing, Steffen Udluft:
A Neural Reinforcement Learning Approach to Gas Turbine Control. IJCNN 2007: 1691-1696 - 2006
- [c4]Daniel Schneegaß, Steffen Udluft, Thomas Martinetz:
Kernel Rewards Regression: An Information Efficient Batch Policy Iteration Approach. Artificial Intelligence and Applications 2006: 428-433 - [c3]Daniel Schneegaß, Thomas Martinetz, Michael Clausohm:
OnlineDoubleMaxMinOver: a simple approximate time and information efficient online Support Vector Classification method. ESANN 2006: 575-580 - [c2]Daniel Schneegaß, Kai Labusch, Thomas Martinetz:
MaxMinOver Regression: A Simple Incremental Approach for Support Vector Function Approximation. ICANN (1) 2006: 150-158 - 2005
- [c1]Thomas Martinetz, Kai Labusch, Daniel Schneegaß:
SoftDoubleMinOver: A Simple Procedure for Maximum Margin Classification. ICANN (2) 2005: 301-306
Coauthor Index
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