Auflistung nach Autor:in "Schmidt-Thieme, Lars"
1 - 3 von 3
Treffer pro Seite
Sortieroptionen
- ZeitschriftenartikelBeyond Manual Tuning of Hyperparameters(KI - Künstliche Intelligenz: Vol. 29, No. 4, 2015) Hutter, Frank; Lücke, Jörg; Schmidt-Thieme, LarsThe success of hand-crafted machine learning systems in many applications raises the question of making machine learning algorithms more autonomous, i.e., to reduce the requirement of expert input to a minimum. We discuss two strategies towards this goal: (1) automated optimization of hyperparameters (including mechanisms for feature selection, preprocessing, model selection, etc) and (2) the development of algorithms with reduced sets of hyperparameters. Since many research directions (e.g., deep learning), show a tendency towards increasingly complex algorithms with more and more hyperparamters, the demand for both of these strategies continuously increases. We review recent hyperparameter optimization methods and discuss data-driven approaches to avoid the introduction of hyperparameters using unsupervised learning. We end in discussing how these complementary strategies can work hand-in-hand, representing a very promising approach towards autonomous machine learning.
- KonferenzbeitragGait verification using deep learning with a pairwise loss(BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group, 2019) Yalavarthi, Vijaya Krishna; Grabocka, Josif; Mandalapu, Hareesh; Schmidt-Thieme, LarsA unique walking pattern to every individual makes gait a promising biometric. Gait is becoming an increasingly important biometric because it can be captured non-intrusively through accelerometers positioned at various locations on the human body. The advent of wearable sensors technology helps in collecting the gait data seamlessly at a low cost. Thus gait biometrics using accelerometers play significant role in security-related applications like identity verification and recognition. In this work, we deal with the problem of identity verification using gait. As the data received through the sensors is indexed in time order, we consider identity verification through gait data as the time series binary classification problem. We present deep learning model with a pairwise loss function for the classification.We conducted experiments using two datasets: publicly available ZJU dataset of more than 150 subjects and our self collected dataset with 15 subjects. With our model, we obtained an Equal Error Rate of 0.05% over ZJU dataset and 0.5% over our dataset which shows that our model is superior to the state-of-the-art baselines.
- KonferenzbeitragWorkshop – Semantische Technologien für Informationsportale(Informatik 2004, Informatik verbindet, Band 2, Beiträge der 34. Jahrestagung der Gesellschaft für Informatik e.V. (GI), 2004) Schmidt-Thieme, Lars; Stumme, Gerd; Rusnak, Ute; Eberhart, Andreas