Support Vector Machines (SVMs) are used to estimate aqueous solubility of organic compounds. A SVM equipped with a Tanimoto similarity kernel estimates solubility with accuracy comparable to results from other reported methods where the same data sets have been studied. Complete cross-validation on a diverse data set resulted in a root-mean-squared error = 0.62 and R(2) = 0.88. The data input t...

OBJECTIVE To compare the performance of two predictive radiologic models, logistic regression (LR) and neural network (NN), with five different resampling methods. METHODS One hundred sixty-seven patients with proven calvarial lesions as the only known disease were enrolled. Clinical and CT data were used for LR and NN models. Both models were developed with cross-validation, leave-one-out, a...

A new method of bandwidth selection for kernel density estimators is proposed. The method, termed indirect cross-validation, or ICV, makes use of so-called selection kernels. Least squares cross-validation (LSCV) is used to select the bandwidth of a selection-kernel estimator, and this bandwidth is appropriately rescaled for use in a Gaussian kernel estimator. The proposed selection kernels are...

In machine learning and its risk-sensitive applications (e.g. medicine, engineering, business), the reliability estimates for individual predictions provide more information about the individual prediction error than the average accuracy of predictive model (e.g. relative mean squared error). Furthermore, they enable the users to distinguish between more and less reliable predictions. The empir...

For microarray based cancer classification, feature selection is a common method for improving classifier generalisation. Most wrapper methods use cross validation methods to evaluate feature sets. For small sample problems like microarray, however, cross validation methods may overfit the data. In this paper, we propose a Structural Risk Minimisation (SRM) based method for gene selection in ca...

We propose robust methods to identify the underlying Partial Differential Equation (PDE) from a given single set of noisy time-dependent data. assume that governing equation PDE is linear combination few and nonlinear differential terms in prescribed dictionary. Noisy data make such identification particularly challenging. Our objective develop against high level noise approximate noise-free dy...

Theories of learning and generalization hold that the generalization bias, defined as the difference between the training error and the generalization error, increases on average with the number of adaptive parameters. This article, however, shows that this general tendency is violated for a gaussian mixture model. For temperatures just below the first symmetry breaking point, the effective num...

Spatial cross-validation and average-error statistics are examined with respect to their abilities to evaluate alternate spatial interpolation methods. A simple crossvalidation methodology is described, and the relative abilities of three, dimensioned error statistics—the root-mean-square error (RMSE), the mean absolute error (MAE), and the mean bias error (MBE)—to describe average interpolator...

The quantitative structure property relationship (QSPR) for supercooled liquid vapour pressures (pL) of polybrominated diphenyl ethers (PBDEs) was investigated. The molecular distance–edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and log pL was modelled by multivariate linear regression (MLR) and an artificial neural network...

changes in groundwater quality, is caused destruction of other resources, either directly or indirectly, due to bad management of groundwater extractions. considering the importance of groundwater resources, especially in arid and semiarid regions, this research was done to modeling the spatial distribution of some groundwater qualitative factors with emphasis on drinking using geostatistical m...