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The fracture characteristics of high strength and ultra high strength concrete were modeled using support vector regression (SVR) by Yuvaraj et al. (2013).
Arabidopsis thaliana has been used as the source to derive plant specific features which were modeled using Support Vector Regression to classify as well as to implement an effective scoring scheme through regression score.
In this case study surface soil texture and coarse fragment classes were predicted using a 28 year time series of Landsat TM derived normalized difference vegetation index (NDVI) and modeled using support vector machine (SVM) classification, and results evaluated relative to more traditional RS approaches (e.g., mono-, bi-, and multi-temporal).
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In this paper, we proposed a novel scheme for sampling good geological models using support vector machine (SVM) and principal component analysis (PCA).
Development of robust calibration models using support vector machines for spectroscopic monitoring of blood glucose.
Each experiment consisted of the training of a kernel-based QSAR model using support vector regression and the ranking of a disjoint screening data set according to the predicted activity.
Using the top 10 clinical attributes and 5 genetic attributes selected by the best models, we were able to build models using support vector machine and random forest to generate high-performance models.
The three-dimensional structure of central and C-terminal regions was modeled using two support structures, one for each region.
For these datasets, we built classification models using support-vector machines (SVM) and Bayesian regularized neural networks (BRANN), trying several different descriptor sets.
According to Rashed (2005a, b) columns or internal walls are modeled using internal supporting cells with the real geometry of their cross sections.
A number of scenarios that included different combinations of the strategies defined during the workshops were modelled using the decision-support tool.
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