This paper studies the ensemble selection problem for unsupervised learning. Given a large library of different clustering solutions, our goal is to select a subset of solutions t...
Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods ...
In recent years, many researchers are studying object categorization problem. It is reported that bag of keypoints approach which is based on local features without topological in...
Ensemble techniques have been successfully applied in the context of supervised learning to increase the accuracy and stability of classification. Recently, analogous techniques fo...
In this paper we address the problem of combining multiple clusterings without access to the underlying features of the data. This process is known in the literature as clustering...