Catarina Silva – författare

This book highlights recent research on Soft Computing, Pattern Recognition, Information Assurance and Security. It presents 38 selected papers from the 10th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018) and the 14th International Conference on Information Assurance and Security (IAS 2018) held at Instituto Superior de Engenharia do Porto (ISEP), Portugal during December 13–15, 2018. SoCPaR – IAS 2018 is a premier conference and brings together researchers, engineers and practitioners whose work involves soft computing and information assurance and their applications in industry and the real world. Including contributions by authors from over 25 countries, the book offers a valuable reference guide for all researchers, students and practitioners in the fields of Computer Science and Engineering.

The two-volume set LNAI 14115 and 14116 constitutes the refereed proceedings of the 22nd EPIA Conference on Progress in Artificial Intelligence, EPIA 2023, held in Faial Island, Azores, in September 2023. The 85 full papers presented in these proceedings were carefully reviewed and selected from 163 submissions.

The two-volume set LNAI 14115 and 14116 constitutes the refereed proceedings of the 22nd EPIA Conference on Progress in Artificial Intelligence, EPIA 2023, held in Faial Island, Azores, in September 2023. The 85 full papers presented in these proceedings were carefully reviewed and selected from 163 submissions.

Text classification is becoming a crucial task to analysts in different areas. In the last few decades, the production of textual documents in digital form has increased exponentially. Their applications range from web pages to scientific documents, including emails, news and books. Despite the widespread use of digital texts, handling them is inherently difficult - the large amount of data necessary to represent them and the subjectivity of classification complicate matters.This book gives a concise view on how to use kernel approaches for inductive inference in large scale text classification; it presents a series of new techniques to enhance, scale and distribute text classification tasks. It is not intended to be a comprehensive survey of the state-of-the-art of the whole field of text classification. Its purpose is less ambitious and more practical: to explain and illustrate some of the important methods used in this field, in particular kernel approaches and techniques.

Text classification is becoming a crucial task to analysts in different areas. In the last few decades, the production of textual documents in digital form has increased exponentially. Their applications range from web pages to scientific documents, including emails, news and books. Despite the widespread use of digital texts, handling them is inherently difficult - the large amount of data necessary to represent them and the subjectivity of classification complicate matters.This book gives a concise view on how to use kernel approaches for inductive inference in large scale text classification; it presents a series of new techniques to enhance, scale and distribute text classification tasks. It is not intended to be a comprehensive survey of the state-of-the-art of the whole field of text classification. Its purpose is less ambitious and more practical: to explain and illustrate some of the important methods used in this field, in particular kernel approaches and techniques.