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(Schlagwörter: "Mustererkennung")

Individual classification of elementary school children's physical activity. A time-efficient […] Kühnhausen, Jan; Dirk, Judith; Schmiedek, Florian Journal Article | In: Behavior Research Methods | 2017 36805 Endnote: Author(s): Kühnhausen, Jan; Dirk, Judith; Schmiedek, Florian
Title: Individual classification of elementary school children's physical activity. A time-efficient group-based approach to reference measurements
In: Behavior Research Methods, 49 (2017) 2, S. 685-697
DOI: 10.3758/s13428-016-0724-2
Publication Type: 3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Language: Englisch
Keywords: Aktivität; Bewegung <Motorische>; Gruppe; Messung; Modellbildung; Mustererkennung; Schüler; Schuljahr 03; Schuljahr 04
Abstract: The objective measurement of physical activity using accelerometers is becoming increasingly popular. There is little consensus, however, about how to analyze acceleration data. One promising approach is the use of reference measurements in which the subjects conduct specific activities. This makes it possible to identify data patterns that indicate these activities for each subject. The drawback of this approach is its rather high cost, in terms of both time and money. We propose a new approach in which a group of children conduct the reference measurements at the same time. We trained support vector machine models on the accelerometer data of 70 children (ages 8-11 years) to predict their activities during those reference measurements. We correctly classified activities with an accuracy of 96.9 % when fitting the individual models for each subject, and 87.5 % when fitting general models for all subjects. The obtained accuracies were comparable to results reported in previous reference measurement studies, in which each subject was measured individually. They were higher than the accuracies obtained by the traditional approach, which transfers accelerometer data to counts and classifies those on the basis of predefined cut points. We concluded that our approach can yield a valuable contribution, particularly to studies with larger samples. (DIPF/Orig.)
DIPF-Departments: Bildung und Entwicklung

Insights from classifying visual concepts with Multiple Kernel Learning Binder, Alexander; Nakajima, Shinichi; Kloft, Marius; Müller, Christina; Samek, Wojciech; […] Journal Article | In: PLoS ONE | 2012 33575 Endnote: Author(s): Binder, Alexander; Nakajima, Shinichi; Kloft, Marius; Müller, Christina; Samek, Wojciech; Brefeld, Ulf; Müller, Klaus-Robert; Kawanabe, Motoaki
Title: Insights from classifying visual concepts with Multiple Kernel Learning
In: PLoS ONE, 7 (2012) 8, S. e38897
DOI: 10.1371/journal.pone.0038897
URL: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0038897
Publication Type: 3a. Beiträge in begutachteten Zeitschriften; Aufsatz (keine besondere Kategorie)
Language: Englisch
Keywords: Algorithmus; Bild; Computer; Daten; Experimentelle Untersuchung; Klassifikation; Lernen; Mustererkennung; Objekt
Abstract: Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, 1-norm regularized MKL variants are often observed to be outperformed by an unweighted sum kernel. The main contributions of this paper are the following: we apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks from the application domain of computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum-kernel SVM and sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. Data sets (kernel matrices) as well as further information are available at http://doc.ml.tu-berlin.de/image_mkl/(Accessed 2012 Jun 25).
DIPF-Departments: Informationszentrum Bildung