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We describe in this paper our approach for the Multi-modal gesture recognition challenge organized by ChaLearn in conjunction with the ICMI 2013 conference. The competition's task was to learn a vocabulary of 20 types of Italian gestures performed from different persons and to detect them in sequences. We develop an algorithm to find the gesture intervals(More)
THIRD EDITION Pharmacopoea internationalis Editio tertia Volume 5 Tests and general requirements for dosage forms Quality specifications for pharmaceutical substances and tablets The International pharmacopoeia. Vol. 5, Tests and general requirements for dosage forms; Quality specifications for pharmaceutical substances and dosage forms. – 3 rd ed. 1.Dosage(More)
Model-based prediction is dependent on many choices ranging from the sample collection and prediction endpoint to the choice of algorithm and its parameters. Here we studied the effects of such choices, exemplified by predicting sensitivity (as IC50) of cancer cell lines towards a variety of compounds. For this, we used three independent sample collections(More)
The purpose of this study was to analyze and compare the degree of step length asymmetry of patients with hip osteoarthritis during free walking and treadmill ambulation and to determine the reproducibility of treadmill based vertical ground reaction force parameters. Twelve subjects with monoarticular hip osteoarthritis undergoing total hip replacement(More)
In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is still computationally challenging. So far, most work relies on stochastic gradient descent (SGD) solvers(More)
Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples. Techniques such as iterative classification or relaxation labeling achieve this by propagating information between related samples during the inference process. When only a few samples are labeled and connections(More)
Bayesian Inference with Markov Chain Monte Carlo (MCMC) has been shown to provide high prediction quality in recommender systems. The advantage over learning methods such as coordinate descent/alternating least-squares (ALS) or (stochastic) gradient descent (SGD) is that MCMC takes uncertainty into account and moreover MCMC can easily integrate priors to(More)
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