Clustering of tree-structured data objects



Performance tests of a novel electroencephalographic data-acquisition system
Usakli, Ali Bulent; Gençer, Nevzat Güneri (2007-02-16)
The aim of the this study is to present some performance tests of a novel 256-channel electroencephalographic data-acquisition system. The common mode rejection ratio of the system was measured as 102 dB for signals in the electroencephalography frequency range and 154 dB for de signals. System electrical noise (referred-to-input) is 1.76 mu V (rms) (0.21 mu V/root Hz for 70-Hz bandwidth). The cross-talk rejection was found to be at 58 dB. The dynamic range of the system was found 108 dB. The performance te...
Multi user support for virtual geogebra teams
Stahl, Gerry; Jim, Ou; Çakır, Murat Perit; Steve, Weimar; Goggins, Sean (null; 2011-07-27)
The Math Forum is an online resource center for pre-algebra, algebra, geometry and pre-calculus. Its Virtual Math Teams (VMT) service provides an integrated web-based environment for small teams to discuss mathematics. The VMT collaboration environment now includes the dynamic mathematics application, GeoGebra. It offers a multi-user version of GeoGebra, which can be used in concert with VMT’s chat, web browsers, curricula and wiki repository.
Sedimentary cyclicity in the upper cretaceous successions of the Haymana Basin (Turkey): depositional sequences as response to relative sea level changes
Huseynov, Afgan; Altıner, Demir; Department of Geological Engineering (2007)
The Haymana basin in Central Anatolia (Turkey) formed on a Late Cretaceous to Middle Eocene fore arc accretionary wedge. The aim of this study is to investigate the sedimentary cyclicity and depositional sequences in the Upper Cretaceous clastic successions of the Haymana basin. To be able to achieve this objective, a 250 m stratigraphic section, which is mainly composed of siliciclastics has been measured in the Haymana Basin. In this study, detailed lithofacies analyses were performed and five different ...
Learning-based methods for multi-modal and multi-spectral data
Özkan, Savaş; Akar, Gözde; Department of Electrical and Electronics Engineering (2020-10-15)
Data-driven solutions have become essential parts of our daily lives. These solutions generally consume a large amount of supervised data. Additionally, a large-body of learnable parameters must be trained to be able to reach the level of human knowledge accurately. However, these dependencies can be overcome by making full use of domain-specific features via specialized learning structures. This thesis addresses unsupervised and multimodal data by utilizing different sensor types and application doma...
Hybrid learning algorithm for intelligent short-term load forecasting
Kumluca Topallı, Ayça; Erkmen, İsmet; Department of Electrical and Electronics Engineering (2003)
Short-term load forecasting (STLF) is an important part of the power generation process. For years, it has been achieved by traditional approaches stochastic like time series; but, new methods based on artificial intelligence emerged recently in literature and started to replace the old ones in the industry. In order to follow the latest developments and to have a modern system, it is aimed to make a research on STLF in Turkey, by neural networks. For this purpose, a method is proposed to forecast Turkey̕s ...
Citation Formats
D. Dinler, M. K. Tural, and N. E. Özdemirel, “Clustering of tree-structured data objects,” presented at the 10th International Statistics Congress (2017), Ankara, Turkey, 2017, Accessed: 00, 2021. [Online]. Available: