Inductive Inference for Large Scale Text Classification

Kernel Approaches and Techniques
1. Auflage 2009
ISBN: 978-3-642-04532-5
Verlag: Springer

Buch, Englisch, Band 255, 155 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 940 g

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Silva, Catarina

Ribeiro, Bernadete

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Geisteswissenschaften Sprachwissenschaft Computerlinguistik, Korpuslinguistik

Inhaltsverzeichnis

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.