A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z
Hans-Georg Zimmermann
- ESANN 2020 - Handling missing data in recurrent neural networks for air quality forecasting [Details]
- ESANN 2002 - Segmental duration control by time delay neural networks with asymmetric causal and retro-causal information flows [Details]
- ESANN 2002 - Undershooting: modeling dynamical systems by time grid refinements [Details]
- ESANN 2002 - Yield curve forecasting by error correction neural networks and partial learning [Details]
- ESANN 1994 - A comparison of neural networks, linear controllers, genetic algorithms and simulated annealing for real time control [Details]
- ESANN 2010 - Autoregressive independent process analysis with missing observations [Details]
- ESANN 1998 - Neural networks for the solution of information-distributed optimal control problems [Details]
- ESANN 2019 - Short-term trajectory planning using reinforcement learning within a neuromorphic control architecture [Details]
- ESANN 2012 - Assessment of sequential Boltmann machines on a lexical processing task [Details]
- ESANN 2012 - Parallelization of Deep Networks [Details]
- ESANN 2015 - A State-Space Model for the Dynamic Random Subgraph Model [Details]
- ESANN 2020 - SDOstream: Low-Density Models for Streaming Outlier Detection [Details]
- ESANN 2015 - Combining dissimilarity measures for prototype-based classification [Details]
- ESANN 2009 - Fuzzy Fleiss-kappa for Comparison of Fuzzy Classifiers [Details]
- ESANN 2009 - Median Variant of Fuzzy c-Means [Details]
- ESANN 2010 - Learning vector quantization for heterogeneous structured data [Details]
- ESANN 2011 - Recent trends in computational intelligence in life sciences [Details]
- ESANN 2014 - Applications of lp-Norms and their Smooth Approximations for Gradient Based Learning Vector Quantization [Details]
- ESANN 2017 - Biomedical data analysis in translational research: integration of expert knowledge and interpretable models [Details]
- ESANN 2024 - Evaluating the Quality of Saliency Maps for Distilled Convolutional Neural Networks [Details]
- ESANN 1995 - Learning the appropriate representation paradigm by circular processing units [Details]
- ESANN 2024 - A Deep Double Q-Learning as a SDLS support in solving LABS problem [Details]
- ESANN 2014 - Naive Augmenting Q-Learning to Process Feature-Based Representations of States [Details]
- ESANN 2012 - Discriminant functional gene groups identification with machine learning and prior knowledge [Details]