Abstract: An iterative method is for determining training data for a primary model to solve a primary recognition task. The iterative method includes a) providing at least one labeled training sample, b) training the primary model with the at least one labeled training sample, c) providing at least one labeled test sample, and d) evaluating a recognition performance of the primary model using the labeled test sample on the primary recognition task. The iterative method further includes, depending on a result of the evaluating the recognition performance, either (i) re-performing parts a), b), c), and d) of the iterative method, or (ii) ending the iterative method.

Abstract: A state, particularly a disease state, associated with the production of volatiles is detected by passing a sample containing the volatiles to a single sensor. This may be a semiconductor gas sensor element or a surface acoustic wave device. This provides an output signal, e.g. in the form of a tailing peak. A plurality of characteristics of the signal (e.g. peak height and maximum positive gradient) are measured to characterise the sample and hence the underlying state. For example we can discriminate between urine samples which are (a) infected with proteus, (b) infected with E. coli or (c) uninfected.