TY - CHAP U1 - Konferenzveröffentlichung A1 - Kopinski, Thomas A1 - Magand, Stéphane A1 - Gepperth, Alexander A1 - Handmann, Uwe T1 - A pragmatic approach to multi-class classification T2 - The International Joint Conference on Neural Networks (IJCNN 2015) N2 - We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class classification. By adding a cascade of additional classifiers, each of which receives the previous classifier's output in addition to regular input data, the approach harnesses unused information that manifests itself in the form of, e.g., correlations between predicted classes. Using multilayer perceptrons as a classification model, we demonstrate the validity of this approach by testing it on a complex ten-class 3D gesture recognition task. Y1 - 2015 UR - https://ieeexplore.ieee.org/document/7280768 UR - http://www.handmann.net/pdf/IJCNN-KopHanEtAl2015a.pdf SN - 2161-4407 SS - 2161-4407 SN - 978-1-4799-1960-4 SB - 978-1-4799-1960-4 U6 - https://doi.org/10.1109/IJCNN.2015.7280768 DO - https://doi.org/10.1109/IJCNN.2015.7280768 VL - 2015 IS - 2015 SP - 1 EP - 8 S1 - 8 ER -