Sample selection method in supervised learning based on adaptive estimated threshold

Machine learning has been used in many areas, such as object detection, pattern recognition, and data dining. Most machine learning algorithms require a high-quality training set. The performance of machine learning would be improved when the informative samples are selected for training. This paper proposes a straightforward definition of boundary samples and provides an effective method to estimate the threshold. The proposed procedure evaluates the weighted average of distance to estimated thresholds for each sample and then selects the boundary samples from initial data. The experimental results show that detectors trained by the selected data have both a higher detection rate and a lower false positive rate compared to the one using training samples which are selected randomly.