New feature selection algorithms for linear threshold functions are de- scribed which combine backward elimination with an adaptive regular- ization method. This makes them particularly suitable to the classiﬁca- tion of microarray expression data, where the goal is to obtain accurate rules depending on few genes only. Our algorithms are fast and easy to implement, since they center on an incremental (large margin) algorithm which allows us to avoid linear, quadratic or higher-order programming methods. We report on preliminary experiments with ﬁve known DNA microarray datasets. These experiments suggest that multiplicative large margin algorithms tend to outperform additive algorithms (such as SVM) on feature selection tasks.