Hans-Günter Meier

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Deep Neural Networks (DNNs) denote multilayer artificial neural networks with more than one hidden layer and millions of free parameters. We propose a Generalized Discriminant Analysis (GerDA) based on DNNs to learn discriminative features of low dimension optimized with respect to a fast classification from a large set of acoustic features for emotion(More)
This paper focuses on the problem of a robust estimation of different transformation matrices based on the well known linear discriminant analysis (LDA) as it is used in automatic speech recognition systems. We investigate the effect of class distributions with artificial features and compare the resulting Fisher criterion. This paper shows that it is not(More)
In this paper, we present a new implementable learning algorithm for the general nonlinear binary classification problem. The suggested algorithm abides the maximum margin philosophy, and learns a decision function from the set of all finite linear combinations of continuous differentiable basis functions. This enables the use of a much more flexible(More)
Industrial robots are widely used in various fields of application. However, when it comes to tasks where high stiffness of the machine is required, usually structural robust machine tools are used instead of industrial robots. Industrial robots, on the other hand, have a high work space and are very versatile in terms of possible applications. The goal of(More)
In this paper, different implementations of elastic joint models of industrial robots are described and compared. The models are intended to be used for roboforming and high speed cutting, respectively, and have been established independently from each other into ADAMS and SimMechanics. To be able to compare the models, they have been adapted to the same(More)
This paper presents a new implementable algorithm for solving the Lipschitz classifier that is a generalization of the maximum margin concept from Hilbert to Banach spaces. In contrast to the support vector machine approach, our algorithm is free to use any finite family of continuously differentiable functions which linearly compose the decision function.(More)
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