Risk estimation using probability machines
نویسندگان
چکیده
منابع مشابه
Probability Machines
J. D. Malley1; J. Kruppa2; A. Dasgupta3; K. G. Malley4; A. Ziegler2 1Center for Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, USA; 2Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Germany; 3Clinical Sciences Section, National Institute of Arthritis and Muscul...
متن کاملProbability Density Estimation Using Advanced Support Vector Machines and the Expectation Maximization Algorithm
This paper presents a new approach for the probability density function estimation using the Support Vector Machines (SVM) and the Expectation Maximization (EM) algorithms. In the proposed approach, an advanced algorithm for the SVM density estimation which incorporates the Mean Field theory in the learning process is used. Instead of using ad-hoc values for the parameters of the kernel functio...
متن کاملEstimation of Probability of Failure with Dependent Variables Using Copulas and Support Vector Machines
This paper presents an approach to estimate probabilities of failure in the case of dependent random variables. The approach is based on copulas and support vector machines (SVMs). A copula is used to generate dependent Monte Carlo samples and an SVM is used to construct the explicit boundary of the failure domain. It is shown that this construction of the failure boundary cannot be made in the...
متن کاملConvex Risk Minimization and Conditional Probability Estimation
This paper proves, in very general settings, that convex risk minimization is a procedure to select a unique conditional probability model determined by the classification problem. Unlike most previous work, we give results that are general enough to include cases in which no minimum exists, as occurs typically, for instance, with standard boosting algorithms. Concretely, we first show that any...
متن کاملExpected shortfall estimation using kernel machines †
In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BioData Mining
سال: 2014
ISSN: 1756-0381
DOI: 10.1186/1756-0381-7-2