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CiteSeerX Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): By far, the support vector machines (SVM) achieve the state-of-theart performance for the text classification (TC) tasks. Due to the complexity of the TC problems, it becomes a challenge to systematically develop classifiers with better performance. We try to attack this problem by ensemble methods, which are often
Support Vector Machine . Support vector machine is based on the structural risk minimization principle. It is reported that SVM outperforms other classifiers in many studies. 21, 22 The SVM approach enjoys many attributes. It is less computationally intense
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May 10, 2005· Boosting SVM Classifiers By Ensemble Yan-Shi Dong Shanghai Jiao Tong University Ke-Song Han Motorola Labs, China Research Center [email protected] ABSTRACT By far, the support vector machines (SVM) achieve the state-of-theart performance for the text classification (TC) tasks. Due to the complexity of the TC problems, it becomes a challenge to systematically develop classifiers
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Support vector machine SVM is a nonlinear classifier which is often reported as producing superior classification results compared to other methods The idea behind the method is to nonlinearly map the input data to some high dimensional space where the data can be linearly separated thus . More + Shanghai Sbm Classifier Liefsliekenl
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