In this paper, an unsupervised-learning method for events-identification in φ-OTDR fiber-optic distributed vibration sensor is proposed. The different vibration-events including blowing, raining, direct and indirect hitting, noise-induced false are clustered by the k-means algorithm. equivalent classification accuracy of 99.4% has been obtained, compared with actual classes experiment. With clu...

This paper proposes an efficient similarity join method using unsupervised learning, when no labeled data is available. In our previous work, we showed that the performance of similarity join could improve when long string attributes, such as paper abstracts, movie summaries, product descriptions, and user feedback, are used under supervised learning, where a training set exists. In this work, ...

White matter lesions are small areas of dead cells found in parts of the brain that act as connectors are detected using magnetic resonance imaging (MRI) which has increasingly been an active and challenging research area in computational neuroscience. This paper presents new image segmentation models for automated detection of white matter changes of the brain in an elderly population. The mai...

In this study we investigate using an unsupervised generative learning method for subjectivity detection in text across different domains. We create an initial training set using simple lexicon information, and then evaluate a calibrated EM (expectation-maximization) method to learn from unannotated data. We evaluate this unsupervised learning approach on three different domains: movie data, ne...

The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing. The high dimensionality of hyperspectral data, presence of substantial noise, and overlap of classes all contribute to the difficulty of automatically clustering and segmenting hyperspectral images. In this article, we propose an unsupervised learning technique that combin...

Automatic Directory Assistance (DA) for business listings poses many application specific problems. One of the main problem is that customers formulate their requests for the same listing with a great variability. This paper presents the results of a study aiming at the evaluation of an approach towards automatic learning, from field data, of expressions typically used by customers to formulate...

to present a multiple-instructor, active-learning strategy in the undergraduate medical curriculum. this educational research is a descriptive one. shared teaching sessions, were designed for undergraduate medical students in six organ-system based courses. sessions that involved in-class discussions of integrated clinical cases were designed implemented and moderated by at least 3 faculties (c...

Supervised polarity classification systems are typically domain-specific. Building these systems involves the expensive process of annotating a large amount of data for each domain. A potential solution to this corpus annotation bottleneck is to build unsupervised polarity classification systems. However, unsupervised learning of polarity is difficult, owing in part to the prevalence of sentime...