Modeling with flexible models, such as neural networks, requires careful control of the model complexity and generalization ability of the resulting model. Whereas general asymptotic estimators of generalization ability have been developed over recent years (e.g., [9]), it is widely acknowledged that in most modeling scenarios there isn't sufficient data available to reliably use these estimato...

The present study evaluates the analytical performance of near infrared as well as attenuated total reflection infrared spectroscopy for the determination of the rosmarinic acid content in Rosmarini folium. Therefore, the recorded near infrared and attenuated total reflection infrared spectra of 42 milled Rosmarini folium samples were correlated with reference data (range: 1.138-2.199 rosmarini...

If the dataset available to machine learning results from cluster sampling (e.g. patients from a sample of hospital wards), the usual cross-validation error rate estimate can lead to biased and misleading results. An adapted cross-validation is described for this case. Using a simulation, the sampling distribution of the generalization error rate estimate, under cluster or simple random samplin...

Experimental MS response factors were measured for 36 different saturated and unsaturated volatile organic compounds (VOC) containing carbon, hydrogen and halogen atoms. Chemical structure was encoded using various molecular descriptors. A quantitative structure-property relationship model was established using the multiple linear regression models. The cross-validation ability of the created m...

Machine learning algorithms search a space of possible hypotheses and estimate the error of each hypotheses using a sample. Most often, the goal of classification tasks is to find a hypothesis with a low true (or generalization) misclassification probability (or error rate); however, only the sample (or empirical) error rate can actually be measured and minimized. The true error rate of the ret...

to prospect mineral deposits at regional scale, recognition and classification of hydrothermal alteration zones using remote sensing data is a popular strategy. due to the large number of spectral bands, classification of the hyperspectral data may be negatively affected by the hughes phenomenon. a practical way to handle the hughes problem is preparing a lot of training samples until the size ...

Integrated Mean Squared Error (IMSE) is a version of the usual mean squared error criterion, averaged over all possible training sets of a given size. If it could be observed, it could be used to determine optimal network complexity or optimal data subsets for efficient training. We show that two common methods of cross-validating average squared error deliver unbiased estimates of IMSE, conver...

Rainfall is considered a highly valuable climatologic resource, particularly in arid regions. As one of the primaryinputs that drive watershed dynamics, rainfall has been shown to be crucial for accurate distributed hydrologicmodeling. Precipitation is known only at certain locations; interpolation procedures are needed to predict this variablein other regions. In this study, the ordinary cokri...