This report analyses the predictive performance of standard techniques for the ‘logical analysis of data’ (LAD), within a probabilistic framework. Improving and extending earlier results, we bound the generalization error of classifiers produced by standard LAD methods in terms of their complexity and how well they fit the training data. We also obtain bounds on the predictive accuracy which de...

A closed-form approximate expression for the optical conductivity of graphene is developed, which generates results with less than 0.8% maximum absolute error for λ>250 nm. The expression takes wavelength, temperature, chemical potential, and hopping parameter into account and provides a fast, easy, and reliable alternative to well-known methods that include singular integrals. Numerical result...

Excessive weight in adults is a national concern with over 2/3 of the US population deemed overweight. Because being overweight has been correlated to numerous diseases such as heart disease and type 2 diabetes, there is a need to understand mechanisms and predict outcomes of weight change and weight maintenance. A simple mathematical model that accurately predicts individual weight change offe...

Non-active adaptive sampling is a way of building machine learning models from training data base which are supposed to dynamically and automatically derive guaranteed sample size. In this context regardless the strategy used in both scheduling generating weak predictors, proposal for calculating absolute convergence error thresholds described. We not only make it possible establish when qualit...