  The test bench time needed for model-based calibration can be reduced with
active learning methods for test design. This paper presents an improved
strategy for active output selection. This is the task of learning multiple
models in the same input dimensions and suits the needs of calibration tasks.
Compared to an existing strategy, we take into account the noise estimate,
which is inherent to Gaussian processes. The method is validated on three
different toy examples. The performance compared to the existing best strategy
is the same or better in each example. In a best case scenario, the new
strategy needs at least 10% less measurements compared to all other active or
passive strategies. Further efforts will evaluate the strategy on a real-world
application. Moreover, the implementation of more sophisticated active-learning
strategies for the query placement will be realized.
