SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery
- M. Atzmüller, F. Puppe
- Computer ScienceEuropean Conference on Principles of Data Mining…
- 18 September 2006
It is shown how SD-Map can handle missing values, and how the algorithm can identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or sampling-based methods.
EuraHS: the development of an international online platform for registration and outcome measurement of ventral abdominal wall hernia repair
- F. Muysoms, G. Campanelli, M. Miserez
- MedicineHernia
- 18 April 2012
An online platform for registration and outcome measurement of abdominal wall hernia repairs with clear definitions and classifications is offered to the surgical community and it is hoped that this registry could lead to better evidence-based guidelines for treatment of abdominalwall hernias based on hernia variables, patient variables, available hernia repair materials and techniques.
Benefits of recruitment in honey bees: effects of ecology and colony size in an individual-based model
- A. Dornhaus, F. Kluegl, Christoph Oechslein, F. Puppe, L. Chittka
- Environmental Science
- 1 May 2006
An individual-based model of honey bee foraging was developed to quantify the benefits of recruitment under different spatial distributions of nondepleting resource patches and with different colony sizes, finding that benefits were strongly dependent on resource patch quality, density, and variability.
Fully Convolutional Neural Networks for Page Segmentation of Historical Document Images
A high-performance fully convolutional neural network for historical document segmentation that is designed to process a single page in one step and introduces a novel metric that is independent of ambiguous ground truth called Foreground Pixel Accuracy (FgPA).
Knowledge reuse among diagnostic problem-solving methods in the Shell-Kit D3
- F. Puppe
- Computer ScienceInt. J. Hum. Comput. Stud.
- 1 October 1998
This work presents an inference structure for diagnostic problem solving optimized with respect to knowledge reuse among methods motivated by experience from large knowledge bases built in medical, technical and other diagnostic domains.
Fast exhaustive subgroup discovery with numerical target concepts
- F. Lemmerich, M. Atzmüller, F. Puppe
- Computer ScienceData mining and knowledge discovery
- 1 May 2016
This paper presents novel techniques for fast exhaustive subgroup discovery with a numerical target concept, and introduces novel bounds in closed form and ordering-based bounds as a new technique to derive estimates for several types of interestingness measures with no previously known bounds.
OCR4all - An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings
- Christian Reul, Dennis Christ, F. Puppe
- Computer ScienceApplied Sciences
- 9 September 2019
The fully automated application on 19th Century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available.
Calamari - A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition
- C. Wick, Christian Reul, F. Puppe
- Computer ScienceDigital Humanities Quarterly
- 5 July 2018
Calamari is a new open source OCR line recognition software that both uses state-of-the art Deep Neural Networks (DNNs) implemented in Tensorflow and giving native support for techniques such as pretraining and voting.
The appearance effect: Influences of virtual agent features on performance and motivation
- Y. Shiban, Iris Schelhorn, A. Mühlberger
- PsychologyComputers in Human Behavior
- 1 August 2015
Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery
- M. Atzmüller, F. Puppe, H. Buscher
- Computer ScienceInternational Joint Conference on Artificial…
- 30 July 2005
This paper categorizes several classes of background knowledge for subgroup discovery, and presents how the necessary knowledge elements can be modelled, and shows how sub group discovery methods benefit from the utilization of backgroundknowledge.
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