Vangelis Karkaletsis

TL;DR: In this article, the authors investigate the performance of two machine learning algorithms in the context of ant-spam filtering and compare them to an alternative memory-based learning approach, after introducing suitable cost-sensitive evaluation measures.

TL;DR: An extensive empirical evaluation of memory-based learning in the context of anti-spam filtering, a novel cost-sensitive application of text categorization that attempts to identify automatically unsolicited commercial messages that flood mailboxes, concludes that memory- based anti- Spam filtering for mailing lists is practically feasible, especially when combined with additional safety nets.

TL;DR: The paper discusses thoroughly the promising paths for future research in medical documents summarization, including the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications.

TL;DR: In this paper, an extensive empirical evaluation of memory-based learning in the context of anti-spam filtering, a novel cost-sensitive application of text categorization that attempts to identify automatically unsolicited commercial messages that flood mailboxes, is performed using a publicly available corpus.

TL;DR: In this paper, the authors evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost sensitive application of text categorization.