TY - CHAP A1 - Stuhlsatz, André A1 - Lippel, Jens A1 - Zielke, Thomas T1 - Feature Extraction for Simple Classification T2 - 2010 20th International Conference on Pattern Recognition, August 23 - 26, 2010 , Istanbul, Turkey Y1 - 2010 SN - 978-0-7695-4109-9 U6 - https://doi.org/10.1109/ICPR.2010.377 SN - 1051-4651 SP - 1525 EP - 1528 PB - IEEE ER - TY - CHAP A1 - Stuhlsatz, André A1 - Meyer, Christine A1 - Eyben, Florian A1 - Zielke, Thomas A1 - Meier, Gunter A1 - Schuller, Bjorn T1 - Deep neural networks for acoustic emotion recognition: Raising the benchmarks T2 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion recognition. On nine frequently used emotional speech corpora, we compare the performance of GerDA features and their subsequent linear classification with previously reported benchmarks obtained using the same set of acoustic features classified by Support Vector Machines (SVMs). Our results impressively show that low-dimensional GerDA features capture hidden information from the acoustic features leading to a significantly raised unweighted average recall and considerably raised weighted average recall. Y1 - 2011 U6 - https://doi.org/10.1109/ICASSP.2011.5947651 SP - 5688 EP - 5691 PB - IEEE ER - TY - CHAP A1 - Stöhr, Marcel A1 - Zielke, Thomas ED - Leipzig, Konrad von ED - Sacks, Natasha ED - Mc Clelland, Michelle T1 - Machine Learning for Soft Sensors and an Application in Cement Production T2 - Smart, Sustainable Manufacturing in an Ever-Changing World Proceedings of International Conference on Competitive Manufacturing (COMA ’22) KW - Soft sensors KW - Machine learning KW - Dynamic processes KW - Cement production KW - Robustness KW - Artificial intelligence Y1 - 2023 SN - 978-3-031-15604-5 U6 - https://doi.org/10.1007/978-3-031-15602-1_46 VL - Lecture Notes in Production Engineering SP - 627 EP - 638 PB - Springer Nature Switzerland CY - Cham ER - TY - CHAP A1 - Zielke, Thomas A1 - Bernst, Irina A1 - Crombach, Tobias A1 - Becker, Martin A1 - Lippel, Jens ED - Gómez Chova, Luis ED - López Martínez, A. ED - Candel Torres, I. T1 - WHAT CAN BE LEARNED BY GRADING ANALYTICS? T2 - INTED 2018: 12th International Technology, Education and Development Conference : 5-7 March, 2018, Valencia (Spain) : conference proceedings Y1 - 2018 SN - 978-84-697-9480-7 U6 - https://doi.org/10.21125/inted.2018.0593 VL - 12th International Technology, Education and Development Conference SP - 3097 EP - 3106 PB - IATED Academy CY - Valencia, Spain ER - TY - CHAP A1 - Lippel, Jens A1 - Becker, Martin A1 - Zielke, Thomas ED - Obaidat, Mohammad S. ED - Ören, Tuncer ED - Szczerbicka, Helena T1 - Modeling Dynamic Processes with Deep Neural Networks: A Case Study with a Gas-fired Absorption Heat Pump T2 - SIMULTECH 2019: Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications : Prague, Czech RepublicJuly 29-31, 2019 Y1 - 2019 SN - 978-989-758-381-0 U6 - https://doi.org/10.5220/0007932903170326 SP - 317 EP - 326 PB - INSTICC ER - TY - CHAP A1 - Becker, Martin A1 - Lippel, Jens A1 - Zielke, Thomas ED - Vezzani, Roberto T1 - Dimensionality Reduction for Data Visualization and Linear Classification, and the Trade-off between Robustness and Classification Accuracy T2 - Proceedings of ICPR 2020: 25th International Conference on Pattern Recognition, 10-15 January 2021, Milan Y1 - 2021 SN - 978-1-72818-808-9 U6 - https://doi.org/10.1109/ICPR48806.2021.9412865 SP - 6478 EP - 6485 PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Becker, Martin A1 - Lippel, Jens A1 - Zielke, Thomas T1 - Gradient Descent Analysis: On Visualizing the Training of Deep Neural Networks T2 - VISIGRAPP 2019: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications : Prague, Czech Republic, February 25-27, 2019 Y1 - 2019 SN - 978-989-758-354-4 U6 - https://doi.org/10.5220/0007583403380345 VL - Volume: 3 SP - 338 EP - 345 PB - INSTICC ER - TY - CHAP A1 - Stuhlsatz, André A1 - Lippel, Jens A1 - Zielke, Thomas T1 - Discriminative feature extraction with Deep Neural Networks T2 - The 2010 International Joint Conference on Neural Networks (IJCNN), 18.07.2010 - 23.07.2010, Barcelona, Spain Y1 - 2010 UR - http://ieeexplore.ieee.org/document/5596329/ SN - 978-1-4244-6916-1 U6 - https://doi.org/10.1109/IJCNN.2010.5596329 SP - 1 EP - 8 PB - IEEE ER - TY - CHAP A1 - Goebel, Johannes A1 - Frank, Lena A1 - Zielke, Thomas A1 - Becker, Martin A1 - Lippel, Jens ED - Deutscher Kälte- und Klimatechnischer Verein, T1 - Modellierung dynamischer Prozesse mit Deep Neural Networks am Beispiel einer Gas-Absorptionswärmepumpe T2 - Deutsche Kaelte-und Klimatagung 2017 Bremen KW - Abwasser-Gas-WP/KM KW - ZIES Y1 - 2017 SP - 1147 EP - 1160 PB - Curran Associates, Inc. CY - Red Hook ER - TY - JOUR A1 - Stuhlsatz, André A1 - Lippel, Jens A1 - Zielke, Thomas T1 - Feature extraction with deep neural networks by a generalized discriminant analysis JF - IEEE transactions on neural networks and learning systems N2 - We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used. Y1 - 2012 U6 - https://doi.org/10.1109/tnnls.2012.2183645 VL - 23 IS - 4 SP - 596 EP - 608 PB - IEEE ER -