TY - CHAP A1 - Bierl, Rudolf A1 - Boehm, M. A1 - Fraunhoffer, Judith A1 - Gardeia, Armin A1 - Mottok, Jürgen A1 - Schneider, Ralph A1 - Streubel, J. T1 - “Regensburger Modell zu den P-Seminaren” – Ein Konzept zur Gestaltung der Schnittstelle Gymnasium – Hochschule und zur Praxis einer kooperativen Lehr-Lern-Didaktik für Informationstechnik T2 - 3rd Embedded Software Engineering Congress, 7. bis 9. Dezember, Sindelfingen Y1 - 2010 PB - Vogel Business Media CY - Würzburg ER - TY - CHAP A1 - Gschossmann, Andreas A1 - Jobst, Simon A1 - Mottok, Jürgen A1 - Bierl, Rudolf T1 - A Measure of Confidence of Artificial Neural Network Classifiers T2 - ARCS Workshop 2019; 32nd International Conference on Architecture of Computing Systems, 20-21 May 2019, Copenhagen, Denmark N2 - Confidence in results of an Artificial Neural Networks (ANNs) is increased by preferring to reject data, that is not trustful, instead of risking a misclassification. For this purpose a model is proposed that is able to recognize data, which differs significantly from the training data, during inference. The proposed model observes all activations of the hidden layers, as well as input and output layers of an ANN in a grey-box view. To make ANNs more robust in safety critical applications, this model can be used to reject flawed data, that is suspected to decrease the accuracy of the model. If this information is logged during inference, it can be used to improve the model, by training it specifically with the missing information. An experiment on the MNIST dataset is conducted and its results are discussed. Y1 - 2019 UR - https://ieeexplore.ieee.org/document/8836211 SP - 153 EP - 157 PB - VDE-Verlag CY - Berlin ER -