TY  - BOOK
AU  - Silva,Catarina
AU  - Ribeiro,Bernardete
ED  - SpringerLink (Online service)
TI  - Inductive Inference for Large Scale Text Classification: Kernel Approaches and Techniques 
T2  - Studies in Computational Intelligence,
SN  - 9783642045332
AV  - TA329-348 
U1  - 519 23
PY  - 2010///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Engineering
KW  - Artificial intelligence
KW  - Text processing (Computer science
KW  - Computational linguistics
KW  - Engineering mathematics
KW  - Appl.Mathematics/Computational Methods of Engineering
KW  - Document Preparation and Text Processing
KW  - Computational Linguistics
KW  - Artificial Intelligence (incl. Robotics)
N1  - 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
N2  - 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
UR  - http://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-04533-2
ER  -