The frequent items problem is to process a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in d...
Knowledge discovery on social network data can uncover latent social trends and produce valuable findings that benefit the welfare of the general public. A growing amount of resea...
This work focuses on one of the most critical issues to plague the wireless telecommunications industry today: the loss of a valuable subscriber to a competitor, also defined as ch...
Jorge Ferreira, Marley B. R. Vellasco, Marco Aur&e...
—We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
Mining bilingual data (including bilingual sentences and terms1 ) from the Web can benefit many NLP applications, such as machine translation and cross language information retrie...
Long Jiang, Shiquan Yang, Ming Zhou, Xiaohua Liu, ...