Samui, Pijush and Sitharam, TG (2010) Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth. In: Mathematical Geosciences, 42 (4). pp. 433-446.
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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 sqa <.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 epsilon-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 ofRVM over the SVM model.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Springer.|
|Keywords:||Support vector machine;Relevance vector machine;Rock depth; Spatial variability|
|Department/Centre:||Division of Mechanical Sciences > Civil Engineering|
|Date Deposited:||11 May 2010 10:46|
|Last Modified:||19 Sep 2010 06:06|
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