排序方式: 共有51条查询结果,搜索用时 437 毫秒
51.
Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In
this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sq⋅km.
Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in
the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that
is firmly based on the theory of statistical learning theory uses regression technique by introducing ε-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training
takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for
spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability of RVM over
the SVM model. 相似文献