—A simple yet effective unsupervised classification rule to discriminate between normal and abnormal data is based on accepting test objects whose nearest neighbors’ distances ...
A new algorithm is proposed for performing unsupervised tissue classification in medical images by integrating conventional clustering techniques with edge-adaptive segmentation t...
This paper introduces an unsupervised morphological segmentation algorithm that shows robust performance for four languages with different levels of morphological complexity. In p...
This paper presents a method that conbines a set of unsupervised algorithms in order to accurately build large taxonomies from any machine-readable dictionary (MRD). Our aim is to...
The mismatch between training and test environmental conditions presents a challenge to speech recognition systems. In this paper, we investigate an approach for matching the dist...