632 research outputs found
Regression depth and support vector machine
The regression depth method (RDM) proposed by Rousseeuw and Hubert [RH99] plays an important role in the area of robust regression for a continuous response variable. Christmann and Rousseeuw [CR01] showed that RDM is also useful for the case of binary regression. Vapnik?s convex risk minimization principle [Vap98] has a dominating role in statistical machine learning theory. Important special cases are the support vector machine (SVM), [epsilon]-support vector regression and kernel logistic regression. In this paper connections between these methods from different disciplines are investigated for the case of pattern recognition. Some results concerning the robustness of the SVM and other kernel based methods are given. --
Insurance: an R-Program to Model Insurance Data
Data sets from car insurance companies often have a high-dimensional complex dependency structure. The use of classical statistical methods such as generalized linear models or Tweedie?s compound Poisson model can yield problems in this case. Christmann (2004) proposed a general approach to model the pure premium by exploiting characteristic features of such data sets. In this paper we describe a program to use this approach based on a combination of multinomial logistic regression and [epsilon]-support vector regression from modern statistical machine learning. --Claim size,insurance tariff,logistic regression,statistical machine learning,support vector regression
Christmann C. — Le parasitisme chez les plantes. Paris, 1960, Collection Armand Colin, n° 338,
Jovet Paul. Christmann C. — Le parasitisme chez les plantes. Paris, 1960, Collection Armand Colin, n° 338,. In: La Terre et La Vie, Revue d'Histoire naturelle, tome 15, n°1, 1961. pp. 167-170
Sur un passage du Tristan de Béroul
Christmann Hans Helmut. Sur un passage du Tristan de Béroul. In: Romania, tome 80 n°317, 1959. pp. 85-87
Development and Utilization of an E-learning Course on Heat Exchangers at ENSIC
International audienceThis paper deals with the development and utilization of an e-learning course at ENSIC in France. Some definitions and examples of problem based learning (PBL) or e-learning utilizations in the world of chemical engineering are first given. This survey results from discussions held in the frame of the Working Party on Education of the European Federation of Chemical Engineering. The e-learning course developed at ENSIC is described and its use, according to an original pedagogy mixing e-learning and PBL, is detailed. The results show that this new pedagogy does not reduce the time of training but induces much more active learning, a better comprehension of technology and the possibility for the students to progress at their own rhythm. Author(s): E. Schaer 1, *, | C. Roizard 2, | N. Christmann 3, | A. Lemaitre
Zwei altfranzosiche Fabels (« Auberee », « Du Vilain mire »), éd. Hans Helmut Christmann. 2e éd
Jodogne Omer. Zwei altfranzosiche Fabels (« Auberee », « Du Vilain mire »), éd. Hans Helmut Christmann. 2e éd. In: Cahiers de civilisation médiévale, 18e année (n°70), Avril-juin 1975. pp. 157-158
Altersstudien und Studien mit alter(n)swissenschaftlichem Analysepotential. Eine vergleichende Kurzübersicht.
Motel-Klingebiel A, Hoff A, Christmann S, Hämel K. Altersstudien und Studien mit alter(n)swissenschaftlichem Analysepotential. Eine vergleichende Kurzübersicht. Diskussionspapiere aus dem Deutschen Zentrum für Altersfragen. Berlin: Deutsches Zentrum für Altersfragen DZA; 2003
Zwei altfranzösische Fabeln (Auberee, Du Vilain mire), neu hrgb. von H. H. Christmann (Sammlg rom. Uebungstexte, 47)
Jodogne Pierre. Zwei altfranzösische Fabeln (Auberee, Du Vilain mire), neu hrgb. von H. H. Christmann (Sammlg rom. Uebungstexte, 47). In: Scriptorium, Tome 30 n°1, 1976. p. 180
Henning Kaufmann : Pfälzische Ortsnamen, Berichtigungen und Ergänzungen zu Ernst Christmann «Die Siedlungsnamen der Pfalz». Wilhelm Fink Verlag, Munich 1971
Schmittlein Raymond. Henning Kaufmann : Pfälzische Ortsnamen, Berichtigungen und Ergänzungen zu Ernst Christmann «Die Siedlungsnamen der Pfalz». Wilhelm Fink Verlag, Munich 1971. In: Revue Internationale d'Onomastique, 24e année N°1, janvier 1972. pp. 75-78
Robust Learning from Bites
Many robust statistical procedures have two drawbacks. Firstly, they are computer-intensive such that they can hardly be used for massive data sets. Secondly, robust confidence intervals for the estimated parameters or robust predictions according to the fitted models are often unknown. Here, we propose a general method to overcome these problems of robust estimation in the context of huge data sets. The method is scalable to the memory of the computer, can be distributed on several processors if available, and can help to reduce the computation time substantially. The method additionally offers distribution-free confidence intervals for the median of the predictions. The method is illustrated for two situations: robust estimation in linear regression and kernel logistic regression from statistical machine learning. --
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