NDLI: Support vector machines for pattern recognition

Content Provider	Semantic Scholar
Author	Tie-Jun, Wu
Copyright Year	2003
Abstract	An overview of the basic ideas underlying Support Vector Machine (SVM) for pattern recognition was given. New methods such as ν - SVM, LS- SVM, weighted SVM and direct SVM, training algorithms including chunking method and decomposition method for the sake of fast computational speed and ease of implementation, model selection approaches minimizing the generalization error, and multiclass classification methods such as one- against- the- rest method, one- against- one method, M- ary classification were concentrated. Finally, an example of multiclass classification for monitoring operation status of wastewater treatment processes was given. As a direct implementation of the structure risk minimization (SRM) inductive principle, SVM provides good performances such as global optimization and good generalization ability.
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Resource Type	Article

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