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» Bayesian Generalized Kernel Mixed Models
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ICML
2004
IEEE
14 years 6 months ago
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm
In kernel methods, an interesting recent development seeks to learn a good kernel from empirical data automatically. In this paper, by regarding the transductive learning of the k...
Zhihua Zhang, Dit-Yan Yeung, James T. Kwok
PKDD
2009
Springer
184views Data Mining» more  PKDD 2009»
14 years 6 days ago
Learning Preferences with Hidden Common Cause Relations
Abstract. Gaussian processes have successfully been used to learn preferences among entities as they provide nonparametric Bayesian approaches for model selection and probabilistic...
Kristian Kersting, Zhao Xu
JAT
2007
56views more  JAT 2007»
13 years 5 months ago
Multiple orthogonal polynomials of mixed type and non-intersecting Brownian motions
We present a generalization of multiple orthogonal polynomials of type I and type II, which we call multiple orthogonal polynomials of mixed type. Some basic properties are formul...
Evi Daems, Arno B. J. Kuijlaars
FDL
2005
IEEE
13 years 11 months ago
SystemC-WMS: A Wave Mixed Signal Simulator
This paper proposes a methodology for extending SystemC to mixed signal systems, aimed at allowing the reuse of analog models and to the simulation of heterogeneous systems. To th...
Simone Orcioni, Giorgio Biagetti, Massimo Conti
ICML
2005
IEEE
14 years 6 months ago
Preference learning with Gaussian processes
In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relat...
Wei Chu, Zoubin Ghahramani