Text Classification, or the task of automatically assigning
semantic categories to natural language text, has become one of the
key methods for organizing online information. Since hand-coding
classification rules is costly or even impractical, most modern
approaches employ machine learning techniques to automatically
learn text classifiers from examples. However, none of these
conventional approaches combines good prediction performance,
theoretical understanding, and efficient training algorithms.
Based on ideas from Support Vector Machines (SVMs), Learning To
Classify Text Using Support Vector Machines presents a new approach
to generating text classifiers from examples. The approach combines
high performance and efficiency with theoretical understanding and
improved robustness. In particular, it is highly effective without
greedy heuristic components. The SVM approach is computationally
efficient in training and classification, and it comes with a
learning theory that can guide real-world applications.
Learning To Classify Text Using Support Vector Machines gives a
complete and detailed description of the SVM approach to learning
text classifiers, including training algorithms, transductive text
classification, efficient performance estimation, and a statistical
learning model of text classification. In addition, it includes an
overview of the field of text classification, making it
self-contained even for newcomers to the field. This book gives a
concise introduction to SVMs for pattern recognition, and it
includes a detailed description of how to formulate
text-classification tasks for machine learning.
Learning To Classify Text Using Support Vector Machines
isdesigned as a reference for researchers and practitioners, and is
suitable as a secondary text for graduate-level students in
Computer Science within Machine Learning and Language
Technology.
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