Active learning favoring points near the border between clusters
Chunjiang Fu, Yupu Yang · Advances in computer science research · 2015
An active learning SVM technique taking advantage of the cluster assumption was proposed.In each active learning iteration, unlabeled instances in the SVM margin were first grouped into two clusters.Then from each cluster, points most similar to the other cluster were selected for labeling.Such points lying near the border between clusters were expected to become support vectors with higher probability.The clustering process was performed in the same kernel space as SVM.With semi-supervised K-medoids, labeled instances were also used to improve the clustering performance.Experiments showed that the proposed method was efficient and robust (to poor initial samples).