Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Breiteneder, Christian"'
Autor:
Slijepcevic, Djordje, Horst, Fabian, Simak, Marvin, Lapuschkin, Sebastian, Raberger, Anna-Maria, Samek, Wojciech, Breiteneder, Christian, Schöllhorn, Wolfgang I., Zeppelzauer, Matthias, Horsak, Brian
Publikováno v:
Gait & Posture 97 (Supplement 1) (2022) 252-253
Machine learning (ML) models have proven effective in classifying gait analysis data, e.g., binary classification of young vs. older adults. ML models, however, lack in providing human understandable explanations for their predictions. This "black-bo
Externí odkaz:
http://arxiv.org/abs/2211.17016
Autor:
Horst, Fabian, Slijepcevic, Djordje, Zeppelzauer, Matthias, Raberger, Anna-Maria, Lapuschkin, Sebastian, Samek, Wojciech, Schöllhorn, Wolfgang I., Breiteneder, Christian, Horsak, Brian
Publikováno v:
Gait & Posture 81 (Supplement 1) (2020) 159-160
State-of-the-art machine learning (ML) models are highly effective in classifying gait analysis data, however, they lack in providing explanations for their predictions. This "black-box" characteristic makes it impossible to understand on which input
Externí odkaz:
http://arxiv.org/abs/2211.17015
Autor:
Slijepcevic, Djordje, Horst, Fabian, Lapuschkin, Sebastian, Raberger, Anna-Maria, Zeppelzauer, Matthias, Samek, Wojciech, Breiteneder, Christian, Schöllhorn, Wolfgang I., Horsak, Brian
Machine learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, namely their black-box cha
Externí odkaz:
http://arxiv.org/abs/1912.07737
Autor:
Slijepcevic, Djordje, Zeppelzauer, Matthias, Gorgas, Anna-Maria, Schwab, Caterine, Schüller, Michael, Baca, Arnold, Breiteneder, Christian, Horsak, Brian
This article proposes a comprehensive investigation of the automatic classification of functional gait disorders based solely on ground reaction force (GRF) measurements. The aim of the study is twofold: (1) to investigate the suitability of stateof-
Externí odkaz:
http://arxiv.org/abs/1712.06405
Media content in large repositories usually exhibits multiple groups of strongly varying sizes. Media of potential interest often form notably smaller groups. Such media groups differ so much from the remaining data that it may be worthy to look at t
Externí odkaz:
http://arxiv.org/abs/1709.10330
In real-world application scenarios, the identification of groups poses a significant challenge due to possibly occurring outliers and existing noise variables. Therefore, there is a need for a clustering method which is capable of revealing the grou
Externí odkaz:
http://arxiv.org/abs/1709.10012
Autor:
Ortner, Thomas, Hoffmann, Irene, Filzmoser, Peter, Rohm, Maia, Breiteneder, Christian, Brodinova, Sarka
A novel approach for supervised classification analysis for high dimensional and flat data (more variables than observations) is proposed. We use the information of class-membership of observations to determine groups of observations locally describi
Externí odkaz:
http://arxiv.org/abs/1709.02942
In this paper, we propose a novel approach for outlier detection, called local projections, which is based on concepts of Local Outlier Factor (LOF) (Breunig et al., 2000) and RobPCA (Hubert et al., 2005). By using aspects of both methods, our algori
Externí odkaz:
http://arxiv.org/abs/1708.01550
A powerful data transformation method named guided projections is proposed creating new possibilities to reveal the group structure of high-dimensional data in the presence of noise variables. Utilising projections onto a space spanned by a selection
Externí odkaz:
http://arxiv.org/abs/1702.06790
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