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A TBL based post-processing approach is proposed for Japanese named entity recognition (NER) in this paper. Firstly, tuning rules are automatically acquired from the results of Japanese NER by error-driven learning. And then, the tuning rules are optimized according to given threshold conditions. After filtered, the rules are used to revise the results of Japanese NER. Above all, this approach could...
A novel method is presented in this paper to study the use of SVM classifiers for multiple feature classification. While commonly multiple binary SVM classifiers are trained on features individually and the outputs of the classifiers are linearly combined for multiple feature classification, our method trains and combines these classifiers simultaneously with lower complexity. To obtain the optimal/suboptimal...
To solve the disadvantage that BP neural network is liable to get into the local minimum, a novel learning algorithm that new chaos optimizer BP neural network is proposed. By the use of the properties of ergodicity and randomness of chaos algorithms, and combining global rough search and local elaborate search of chaotic variable, get the global optimization weight values of neural network. By the...
This paper proposes an effective method for constructing and pruning support vector machine ensembles for improved classification performance. Firstly we propose a novel method for constructing SVM ensembles. Traditionally an SVM ensemble is constructed by the data sampling method; In our method, however,each individual SVM classifier is trained by using the same original training set, but with different...
Decision tree is one common method used in data mining to extract predicted information. Based on Statistical Learning Theory (SLT), support vector machine(SVM) is a new kind of machine learning method that is used for classification and regression, it realizes the trade-off between empirical risk minimization(ERM) and generalization capability. SVM and decision tree have combined into one multi-class...
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