We view the problem of estimating the defect content of a document after an inspection as a machine learning problem: The goal is to learn from empirical data the relationship between certain observable features of an inspection (such as the total number of different defects detected) and the number of defects actually contained in the document. We show that some features can carry significant nonlinear information about the defect content. Therefore, we use a nonlinear regression technique, neural networks, to solve the learning problem. To select the best among all neural networks trained on a given data set, one usually reserves part of the data set for later cross-validation; in contrast, we use a technique which leaves the full data set for training. This is an advantage when the data set is small. We validate our approach on a known empirical inspection data set. For that benchmark, our novel approach clearly outperforms both linear regression and the current standard methods in software engineering for estimating the defect content, such as capture-recapture. The validation also shows that our machine learning approach can be successful even when the empirical inspection data set is small.

A new model for chemosensory reception is presented. It models reacti(cid:173) ons between odor molecules and receptor proteins and the activation of second messenger by receptor proteins. The mathematical formulation of the reaction kinetics is transformed into an artificial neural network (ANN). The resulting feed-forward network provides a powerful means for parameter fitting by applying learning algorithms. The weights of the network corresponding to chemical parameters can be trained by presen(cid:173) ting experimental data. We demonstrate the simulation capabilities of the model with experimental data from honey bee chemosensory neurons. It can be shown that our model is sufficient to rebuild the observed data and that simpler models are not able to do this task.