Regeneration and multiplication of germplasm resources in seed genebanks: The scientific background

The regeneration of germplasm resources is an integral part of the conservation programme. This study is directed towards the strategy and tactics of seed regeneration and multiplication by sexual reproduction in relation to problems of maintaining the genetic constitution of the original sampled population

Pairwise Preference Learning and Ranking

We consider supervised learning of a ranking function, which is a mapping from instances to total orders over a set of labels (options). The training information consists of examples with partial (and possibly inconsistent) information about their associated rankings. From these, we induce a ranking function by reducing the original problem to a number of binary classification problems, one for each pair of labels. The main objective of this work is to investigate the trade-off between the quality of the induced ranking function and the computational complexity of the algorithm, both depending on the amount of preference information given for each example. To this end, we present theoretical results on the complexity of pairwise preference learning, and experimentally investigate the predictive performance of our method for different types of preference information, such as top-ranked labels and complete rankings. The domain of this study is the prediction of a rational agent's ranking of actions in an uncertain environment