Based mostly totally on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a model new technique to producing textual content material classifiers from examples. The technique combines high effectivity and effectivity with theoretical understanding and improved robustness. Notably, it could be very environment friendly with out greedy heuristic parts. The SVM technique is computationally setting pleasant in teaching and classification, and it comes with a learning precept which will info precise-world functions.
Learning To Classify Text Using Support Vector Machines supplies a whole and detailed description of the SVM technique to learning textual content material classifiers, along with teaching algorithms, transductive textual content material classification, setting pleasant effectivity estimation, and a statistical learning model of textual content material classification. In addition to, it consists of an abstract of the sector of textual content material classification, making it self-contained even for newcomers to the sector. This book supplies a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate textual content material-classification duties for machine learning.
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