Inductive Inference for Large Scale Text Classification Kernel Approaches and Techniques /

• Κύριοι συγγραφείς: Silva, Catarina (Συγγραφέας), Ribeiro, Bernardete (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2010.
• Σειρά: Studies in Computational Intelligence, 255
• Θέματα: Engineering.; Artificial intelligence.; Text processing (Computer science).; Computational linguistics.; Applied mathematics.; Engineering mathematics.; Appl.Mathematics/Computational Methods of Engineering.; Document Preparation and Text Processing.; Computational Linguistics.; Artificial Intelligence (incl. Robotics).

 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. 

 Fundamentals -- Background on Text Classification -- Kernel Machines for Text Classification -- Approaches and techniques -- Enhancing SVMs for Text Classification -- Scaling RVMs for Text Classification -- Distributing Text Classification in Grid Environments -- Framework for Text Classification.