Bayesian methods for adaptive models
Bayesian methods for adaptive models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
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This paper examines the potential of relevance vector machine (RVM) for slope reliability by using first order second moment method (FOSM). The FOSM demands the values and partial derivatives of the performance function with respect to the design random variables. Such calculations could be time-consuming or cumbersome when the performance functions are implicit. The analysis of slope by limit equilibrium method gives implicit performance functions. Here, RVM has been used to predict implicit performance functions. RVM rely on the Bayesian concept and utilize an inductive modelling procedure that allows incorporation of prior knowledge in the estimation process. In this paper, an example is given regarding how the proposed RVM-based FOSM analysis can be carried out. This study shows that the proposed RVM-based FOSM is viable alternative for slope reliability analysis.