Publikationen


Suche nach „[2012]“ hat 264 Publikationen gefunden
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    Maschinenbau und Mechatronik

    Vortrag

    Simone Walker-Hertkorn

    Auslegung und Genehmigungspraxis für Erdwärmesonden

    3. Wärmepumpen-Forum der Firma Viessmann, Allendorf

    2012

    Maschinenbau und Mechatronik

    Vortrag

    Simone Walker-Hertkorn

    Risiko und Gefährdungsanalyse am Beispiel Geothermie

    geofora 2012 - Fachmesse und Kongress für Brunnenbau, Bohrtechnik und Geothermie, Hof

    2012

    Maschinenbau und Mechatronik

    Vortrag

    Simone Walker-Hertkorn

    Geothermie eine nachhaltige Energienutzung

    Nachhaltigkeitsforum beim Bundesamt für Geodäsie, Bad Kötzting

    2012

    Maschinenbau und Mechatronik

    Zeitschriftenartikel

    A. Gordon, Z. Lemeš, Simone Walker-Hertkorn, D. Kuntz

    Erste hessische Tiefbohrung einer koaxialen Tiefen-Erdwärmesonde

    bbr - Fachmagazin für Brunnen- und Leitungsbau (Sonderausgabe Oberflächennahe Geothermie), vol. 63

    2012

    Elektrotechnik und Medientechnik

    Vortrag

    E.-M. Ascherl, G. Feneberg, Gerhard Krump

    Anwendung des Messverfahrens PEAQ bei Produkttests und Audiocodecs

    DAGA 2012: Fortschritte der Akustik (38. Deutsche Jahrestagung für Akustik), Darmstadt

    2012

    Angewandte Naturwissenschaften und Wirtschaftsingenieurwesen

    Patent

    Raimund Förg

    Method for Manufacturing a Composite Wafer Having a Graphite Core, and Composite Wafer Having a Graphite Core

    2012

    Angewandte Naturwissenschaften und Wirtschaftsingenieurwesen

    Patent

    Raimund Förg

    Method for Manufacturing a Composite Wafer Having a Graphite Core, and Composite Wafer Having a Graphite Core

    2012

    Angewandte Naturwissenschaften und Wirtschaftsingenieurwesen

    Patent

    Raimund Förg

    Ein Verfahren zum Herstellen eines Verbundwafers mit einem Graphitkern und ein Verbundwafer mit einem Graphitkern

    2012

    Angewandte Naturwissenschaften und Wirtschaftsingenieurwesen

    Patent

    Raimund Förg

    Composite wafer having graphite core and method for manufacturing same

    2012

    Angewandte Naturwissenschaften und Wirtschaftsingenieurwesen

    Patent

    Raimund Förg

    Etching device and a method for etching a material of a workpiece

    2012

    Angewandte Naturwissenschaften und Wirtschaftsingenieurwesen

    Patent

    Raimund Förg

    Etching Device and a Method for Etching a Material of a Workpiece

    2012

    TC Grafenau

    Zeitschriftenartikel

    A. Christmann, Robert Hable

    Consistency of support vector machines using additive kernels for additive models

    Computational Statistics & Data Analysis, vol. 56, no. 4, pp. 854-873

    2012

    DOI: 10.1016/j.csda.2011.04.006

    Abstract anzeigen

    Support vector machines (SVMs) are special kernel based methods and have been among the most successful learning methods for more than a decade. SVMs can informally be described as kinds of regularized MM-estimators for functions and have demonstrated their usefulness in many complicated real-life problems. During the last few years a great part of the statistical research on SVMs has concentrated on the question of how to design SVMs such that they are universally consistent and statistically robust for nonparametric classification or nonparametric regression purposes. In many applications, some qualitative prior knowledge of the distribution View the MathML sourceP or of the unknown function ff to be estimated is present or a prediction function with good interpretability is desired, such that a semiparametric model or an additive model is of interest. The question of how to design SVMs by choosing the reproducing kernel Hilbert space (RKHS) or its corresponding kernel to obtain consistent and statistically robust estimators in additive models is addressed. An explicit construction of such RKHSs and their kernels, which will be called additive kernels, is given. SVMs based on additive kernels will be called additive support vector machines . The use of such additive kernels leads, in combination with a Lipschitz continuous loss function, to SVMs with the desired properties for additive models. Examples include quantile regression based on the pinball loss function, regression based on the ϵϵ-insensitive loss function, and classification based on the hinge loss function.

    TC Grafenau

    Zeitschriftenartikel

    Robert Hable

    Asymptotic Normality of Support Vector Machine Variants and Other Regularized Kernel Methods

    Journal of Multivariate Analysis, vol. 106, no. April, pp. 92-117

    2012

    DOI: 10.1016/j.jmva.2011.11.004

    Abstract anzeigen

    In nonparametric classification and regression problems, regularized kernel methods, in particular support vector machines, attract much attention in theoretical and in applied statistics. In an abstract sense, regularized kernel methods (simply called SVMs here) can be seen as regularized M-estimators for a parameter in a (typically infinite dimensional) reproducing kernel Hilbert space. For smooth loss functions LL, it is shown that the difference between the estimator, i.e. the empirical SVM View the MathML sourcefL,Dn,λDn, and the theoretical SVM fL,P,λ0fL,P,λ0 is asymptotically normal with rate View the MathML sourcen. That is, View the MathML sourcen(fL,Dn,λDn−fL,P,λ0) converges weakly to a Gaussian process in the reproducing kernel Hilbert space. As common in real applications, the choice of the regularization parameter View the MathML sourceDn in View the MathML sourcefL,Dn,λDn may depend on the data. The proof is done by an application of the functional delta-method and by showing that the SVM-functional P↦fL,P,λP↦fL,P,λ is suitably Hadamard-differentiable.

    TC Grafenau

    Zeitschriftenartikel

    Ali Fallah Tehrani, W. Cheng, E. Hüllermeier

    Preference Learning using the Choquet Integral: The Case of Multipartite Ranking

    IEEE Transactions on Fuzzy Systems, vol. 20, no. 6, pp. 1102-1113

    2012

    DOI: 10.1109/TFUZZ.2012.2196050

    Abstract anzeigen

    We propose a novel method for preference learning or, more specifically, learning to rank, where the task is to learn a ranking model that takes a subset of alternatives as input and produces a ranking of these alternatives as output. Just like in the case of conventional classifier learning, training information is provided in the form of a set of labeled instances, with labels or, say, preference degrees taken from an ordered categorical scale. This setting is known as multipartite ranking in the literature. Our approach is based on the idea of using the (discrete) Choquet integral as an underlying model for representing ranking functions. Being an established aggregation function in fields such as multiple criteria decision making and information fusion, the Choquet integral offers a number of interesting properties that make it attractive from a machine learning perspective, too. The learning problem itself comes down to properly specifying the fuzzy measure on which the Choquet integral is defined. This problem is formalized as a margin maximization problem and solved by means of a cutting plane algorithm. The performance of our method is tested on a number of benchmark datasets.

    TC Grafenau

    Zeitschriftenartikel

    Ali Fallah Tehrani, W. Cheng, K. Dembczýnski, E. Hüllermeier

    Learning Monotone Nonlinear Models using the Choquet Integral

    Machine Learning, vol. 89, no. 1, pp. 183-211

    2012

    DOI: 10.1007/s10994-012-5318-3

    Abstract anzeigen

    The learning of predictive models that guarantee monotonicity in the input variables has received increasing attention in machine learning in recent years. By trend, the difficulty of ensuring monotonicity increases with the flexibility or, say, nonlinearity of a model. In this paper, we advocate the so-called Choquet integral as a tool for learning monotone nonlinear models. While being widely used as a flexible aggregation operator in different fields, such as multiple criteria decision making, the Choquet integral is much less known in machine learning so far. Apart from combining monotonicity and flexibility in a mathematically sound and elegant manner, the Choquet integral has additional features making it attractive from a machine learning point of view. Notably, it offers measures for quantifying the importance of individual predictor variables and the interaction between groups of variables. Analyzing the Choquet integral from a classification perspective, we provide upper and lower bounds on its VC-dimension. Moreover, as a methodological contribution, we propose a generalization of logistic regression. The basic idea of our approach, referred to as choquistic regression, is to replace the linear function of predictor variables, which is commonly used in logistic regression to model the log odds of the positive class, by the Choquet integral. First experimental results are quite promising and suggest that the combination of monotonicity and flexibility offered by the Choquet integral facilitates strong performance in practical applications.

    Angewandte Wirtschaftswissenschaften

    Zeitschriftenartikel

    Diane Ahrens

    Internationaler Einkäufer. Immense Bedeutung für die Wettbewerbsfähigkeit von Unternehmen.

    All About Sourcing, no. April

    2012

    TC Freyung

    Wolfgang Dorner, Roland Zink, J. Pauli, F. Diepold, C. Fehlner, Anna Marquardt, Raphaela Pagany, R. Rettinger, V. Sigel

    Energiestrategie Zellertal

    Entwicklung einer gemeinsamen Energiestrategie zur Steigerung der Energieeffizienz, zum Energiesparen und zur Planung sowie Nutzung erneuerbarer Energien

    2012

    Elektrotechnik und Medientechnik

    Vortrag

    F. Diepold, Raphaela Pagany

    Wohin mit der Windkraft? Interaktive 3D-Visualisierung mit GIS für eine erfolgreiche Bürgerbeteiligung

    AGIT Symposium & Expo 2012 - Angewandte Geoinformatik, Salzburg, Österreich

    2012

    Bauingenieurwesen und Umwelttechnik

    Zeitschriftenartikel

    Kai Haase

    Holzbau nach DIN EN 1995:2010 und DIN EN 1995/NA:2010

    FRILO-Magazin, pp. 38-45

    2012