Entries by Andreas Holzinger

Machine Learning in Nature again

Lecun, Y., Bengio, Y. & Hinton, G. 2015. Deep learning. Nature, 521, (7553), 436-444. Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains […]

June, 23, 2015 Seminar Talk Machine Learning

Title: Towards Knowledge Discovery with the human in the machine learning loop: An Ontology-Guided Meta-Classifying Approach for the Biomedical Domain Lecturer: Dominic GIRADI, RISC-Software Linz, Austria <expertise> Abstract: The process of knowledge discovery in clinical research is significantly different from other business domains, for example market research. While in the general definitions of knowledge discovery […]

May,19, 2015 Seminar Talks Machine Learning

Title: Towards Personalization of Diabetes Therapy Using Computerized Decision Support and Machine Learning Lecturer: Klaus DONSA <expertise> and Stephan SPAT <expertise> Abstract: Diabetes mellitus (DM) is a growing global disease which highly affects the individual patient and represents a global health burden with financial impact on national health care systems. The therapeutic options include lifestyle […]

Apr, 14, 2015 Seminar Talks Deep Learning

Title:  Using Deep Learning for Discovering Knowledge from Images: Pitfalls and Best Practices Lecturer: Marcus BLOICE <expertise> Abstract: Neural networks have been shown to be adept at image analysis and image classification. Deep layered neural networks especially so. However, deep learning requires two things in order to work proficiently: large amounts of data and lots […]

Open PhD machine learning

PhD position in “Biomedical data sciences and machine learning” + 2 open MSc positions in the context of the new competence center for biomarker discovery cbmed.org located at the Medical University Graz. You … … have a MSc related to Information & Computer Science (e.g. Informatics, Software Engineering, Telematics, Mathematics, …) … are eligible to […]

Geometric, Topological and Harmonic Trends to Image Processing due to 1st June 2015

Special Issue on Geometric, Topological and Harmonic Trends to Image Processing Pattern Recognition Letters Submission deadline: June 1, 2015 Advanced topological measures from the numerical and algebraic perspective, combined with the geometric representations of physical objects and the sparse decomposition using harmonic transforms are generating novel methods for the study of n-dimensional digital or continuous […]

The future is in Open Data Sets

The idea of “open data” is not new. Many researchers in the past had followed the notion that Science is a public enterprise and that certain data should be openly available [1] and it is recently also a big topic in the biomedical domain [2], [3]; e.g.. the British Medical Journal (BMJ) started a big […]

Machine Learning in Nature

Apart from occassional news entries, comptuer science rarely makes it into Nature. A quick count in the Web of Science results in 33 articles, the last one – a year ago – by Ekert, A. & Renner, R. 2014. The ultimate physical limits of privacy. Nature, 507, (7493), 443-447, and the most prominent one surely […]

Feb, 17, 2015 > Seminar Talk by Hubert Wagner

Title: Topological analysis of text data. Lecturer: Hubert WAGNER <expertise> Abstract: In this talk an ongoing effort will be described to apply persistent homology in the area of text data mining. Persistent homology is the main tool of topological data analysis. In essence, it allows to robustly describe the shape of a data set, and […]

January, 27, 2015, Seminar Talk by Barbara Di Fabio

Title: Geometric-topological tools for shape description Lecturer: Barbara DI FABIO Abstract: In shape comparison a widely used scheme is to measure the dissimilarity between signatures associated with each shape rather than matching shapes. In this context, computational topology plays an important role, offering a series of techniques and measures with an extremely high abstraction power. […]