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发表于 2025-3-28 18:37:18
On Multiple Imputation Through Finite Gaussian Mixture ModelsMultiple Imputation is a frequently used method for dealing with partial nonresponse. In this paper the use of finite Gaussian mixture models for multiple imputation in a Bayesian setting is discussed. Simulation studies are illustrated in order to show performances of the proposed method.
胡言乱语
发表于 2025-3-28 22:22:08
The Cine-Tourist’s Map of New Wave Parisilarity measure and a . similarity measure related to the Canberra distance. It is proved that they are positive semi-definite (p.s.d.), thus facilitating their use in kernel-based methods, like the Support Vector Machine, a very popular machine learning tool. These kernels may be better suited than
Monotonous
发表于 2025-3-29 02:16:59
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送秋波
发表于 2025-3-29 04:24:27
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ARC
发表于 2025-3-29 09:20:49
François Penz,Aileen Reid,Maureen Thomas which do not reflect the assessment uncertainty. .-class assessment probabilities are usually generated by using a reduction to binary tasks, univariate calibration and further application of the pairwise coupling algorithm. This paper presents an alternative to coupling with usage of the Dirichlet
remission
发表于 2025-3-29 13:21:26
François Penz,Aileen Reid,Maureen Thomasn invariances are . kernels. Instances such as tangent distance or dynamic time-warping kernels have demonstrated the real world applicability. This motivates the demand for investigating the elementary properties of the general IDS-kernels. In this paper we formally state and demonstrate their inva
Influx
发表于 2025-3-29 18:51:20
https://doi.org/10.1007/978-3-211-99150-3ade of only those variables, which are essential for the differentiation of studied objects. This selection may be made easier if a graphic analysis of an U-matrix is carried out. It allows to easily identify variables, which do not differentiate the studied objects. A graphic analysis may, however,
传染
发表于 2025-3-29 21:32:07
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人类的发源
发表于 2025-3-30 03:35:17
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Seminar
发表于 2025-3-30 06:17:34
https://doi.org/10.1007/978-3-663-01930-5are compared. In order to systematically compare the local methods and LDA as global standard technique, they are applied to a variety of situations which are simulated by experimental design. This way, it is possible to identify characteristics of the data that influence the classification performa