N04-4002 . In this dataset , however , MMR-based feature selection does not produce improvements
N04-4002 text categorization . Besides , MMR-based feature selection method sometimes produces improvements
N04-4002 becomes more critical . <title> MMR-based feature selection for text categorization </title>
N04-4002 Feature Selection We propose a MMR-based feature selection which selects each feature according
N04-4002 for text categorization . Our MMR-based feature selection method strives to reduce redundancy
N04-4002 Reuters after term selection using MMR-based feature selection ( number of features is 25 )
N04-4002 terms ) . In this data set , again MMR-based feature selection has best performance and significant
N04-4002 WebKB are highly consistent . MMR-based feature selection is consistently more effective
N04-4002 feature selection method . Using MMR-based feature selection the best accuracy is 82.47 %
N04-4002 using IG , and then we applied MMR-based feature selection and greedy feature selection
N04-4002 method and SVM using all features . MMR-based feature selection and greedy feature selection
N04-4002 computational cost , we can use MMR-based feature selection method on the reduced feature
N04-4002 In this paper , we proposed a MMR-based feature selection method which strives to reduce
N04-4002 Sim1 or a different metric . 3 MMR-based Feature Selection We propose a MMR-based feature
N04-4002 support 10-fold cross validation . MMR-based feature selection method needs to tune for A .
N04-4002 accuracy . A disadvantage in using MMR-based feature selection is that the computational cost
N04-4002 in section 3 , we describe the MMR-based feature selection . Section 4 presents the in-depth
N04-4002 . Empirical results show that MMR-based feature selection is more effective than Koller
N04-4002 novelty of information . We define MMR-based feature selection as follows : MMR FS _ Arg max
N04-4002 experiment results , we can verify that MMR-based feature selection is more effective than Koller
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